<html><head>
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   <title>Apache OpenNLP Developer Documentation</title><link rel="stylesheet" href="css/opennlp-docs.css" type="text/css"><meta name="generator" content="DocBook XSL-NS Stylesheets V1.75.2"></head><body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div lang="en" class="book" title="Apache OpenNLP Developer Documentation"><div class="titlepage"><div><div><h1 class="title"><a name="d4e1"></a>Apache OpenNLP Developer Documentation</h1></div><div><div class="authorgroup">
			<h3 class="corpauthor">Written and maintained by the Apache OpenNLP Development
				Community</h3>
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			Version 1.7.1
		</p></div><div><p class="copyright">Copyright &copy; 2011, 2024 The Apache Software Foundation</p></div><div><div class="legalnotice" title="Legal Notice"><a name="d4e7"></a>
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				<b>License and Disclaimer.&nbsp;</b>
				
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					to you under the Apache License,
					Version 2.0 (the
					"License"); you may not use this documentation
					except in compliance
					with the License. You may obtain a copy of the
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		</div></div></div><hr></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="chapter"><a href="#opennlp">1. Introduction</a></span></dt><dd><dl><dt><span class="section"><a href="#intro.description">Description</a></span></dt><dt><span class="section"><a href="#intro.general.library.structure">General Library Structure</a></span></dt><dt><span class="section"><a href="#intro.api">Application Program Interface (API). Generic Example</a></span></dt><dt><span class="section"><a href="#intro.cli">Command line interface (CLI)</a></span></dt><dd><dl><dt><span class="section"><a href="#intro.cli.description">Description</a></span></dt><dt><span class="section"><a href="#intro.cli.toolslist">List of tools</a></span></dt><dt><span class="section"><a href="#intro.cli.setup">Setting up</a></span></dt><dt><span class="section"><a href="#intro.cli.generic">Generic Example</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.sentdetect">2. Sentence Detector</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.detection">Sentence Detection</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.detection.cmdline">Sentence Detection Tool</a></span></dt><dt><span class="section"><a href="#tools.sentdetect.detection.api">Sentence Detection API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.sentdetect.training">Sentence Detector Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.sentdetect.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.sentdetect.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.eval.tool">Evaluation Tool</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.tokenizer">3. Tokenizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.introduction">Tokenization</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.cmdline">Tokenizer Tools</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.api">Tokenizer API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.tokenizer.training">Tokenizer Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.tokenizer.detokenizing">Detokenizing</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.detokenizing.api">Detokenizing API</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.detokenizing.dict">Detokenizer Dictionary</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.namefind">4. Name Finder</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.recognition">Named Entity Recognition</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.recognition.cmdline">Name Finder Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.recognition.api">Name Finder API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.training">Name Finder Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.training.api">Training API</a></span></dt><dt><span class="section"><a href="#tools.namefind.training.featuregen">Custom Feature Generation</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.eval.tool">Evaluation Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.eval.api">Evaluation API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.annotation_guides">Named Entity Annotation Guidelines</a></span></dt></dl></dd><dt><span class="chapter"><a href="#tools.doccat">5. Document Categorizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.doccat.classifying">Classifying</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.doccat.classifying.cmdline">Document Categorizer Tool</a></span></dt><dt><span class="section"><a href="#tools.doccat.classifying.api">Document Categorizer API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.doccat.training">Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.doccat.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.doccat.training.api">Training API</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.postagger">6. Part-of-Speech Tagger</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.tagging">Tagging</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.tagging.cmdline">POS Tagger Tool</a></span></dt><dt><span class="section"><a href="#tools.postagger.tagging.api">POS Tagger API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.postagger.training">Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.postagger.training.api">Training API</a></span></dt><dt><span class="section"><a href="#tools.postagger.training.tagdict">Tag Dictionary</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.postagger.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.eval.tool">Evaluation Tool</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.lemmatizer">7. Lemmatizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.lemmatizer.tagging.cmdline">Lemmatizer Tool</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.tagging.api">Lemmatizer API</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.training">Lemmatizer Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.lemmatizer.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.lemmatizer.evaluation">Lemmatizer Evaluation</a></span></dt></dl></dd><dt><span class="chapter"><a href="#tools.chunker">8. Chunker</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.chunking">Chunking</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.chunking.cmdline">Chunker Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.chunking.api">Chunking API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.chunker.training">Chunker Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.chunker.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.chunker.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.chunker.evaluation">Chunker Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.chunker.evaluation.tool">Chunker Evaluation Tool</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.parser">9. Parser</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.parsing">Parsing</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.parsing.cmdline">Parser Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.parsing.api">Parsing API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.parser.training">Parser Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.parser.evaluation">Parser Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.evaluation.tool">Parser Evaluation Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.evaluation.api">Evaluation API</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#tools.coref">10. Coreference Resolution</a></span></dt><dt><span class="chapter"><a href="#tools.extension">11. Extending OpenNLP</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.extension.writing">Writing an extension</a></span></dt><dt><span class="section"><a href="#tools.extension.osgi">Running in an OSGi container</a></span></dt></dl></dd><dt><span class="chapter"><a href="#tools.corpora">12. Corpora</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.conll">CONLL</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.conll.2000">CONLL 2000</a></span></dt><dt><span class="section"><a href="#tools.corpora.conll.2002">CONLL 2002</a></span></dt><dt><span class="section"><a href="#tools.corpora.conll.2003">CONLL 2003</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.arvores-deitadas">Arvores Deitadas</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.getting">Getting the data</a></span></dt><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.converting">Converting the data (optional)</a></span></dt><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.evaluation">Training and Evaluation</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.leipzig">Leipzig Corpora</a></span></dt><dt><span class="section"><a href="#tools.corpora.ontonotes">OntoNotes Release 4.0</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.ontonotes.namefinder">Name Finder Training</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.brat">Brat Format Support</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.brat.webtool">Sentences and Tokens</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.training">Training</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.evaluation">Evaluation</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.cross-validation">Cross Validation</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#opennlp.ml">13. Machine Learning</a></span></dt><dd><dl><dt><span class="section"><a href="#opennlp.ml.maxent">Maximum Entropy</a></span></dt><dd><dl><dt><span class="section"><a href="#opennlp.ml.maxent.impl">Implementation</a></span></dt></dl></dd></dl></dd><dt><span class="chapter"><a href="#org.apche.opennlp.uima">14. UIMA Integration</a></span></dt><dd><dl><dt><span class="section"><a href="#org.apche.opennlp.running-pear-sample">Running the pear sample in CVD</a></span></dt><dt><span class="section"><a href="#org.apche.opennlp.further-help">Further Help</a></span></dt></dl></dd><dt><span class="chapter"><a href="#tools.morfologik-addon">15. Morfologik Addon</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.morfologik-addon.api">Morfologik Integration</a></span></dt><dt><span class="section"><a href="#tools.morfologik-addon.cmdline">Morfologik CLI Tools</a></span></dt></dl></dd><dt><span class="chapter"><a href="#tools.cli">16. The Command Line Interface</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.doccat">Doccat</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.doccat.Doccat">Doccat</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatTrainer">DoccatTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatEvaluator">DoccatEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatCrossValidator">DoccatCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatConverter">DoccatConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.dictionary">Dictionary</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.dictionary.DictionaryBuilder">DictionaryBuilder</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.tokenizer">Tokenizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.tokenizer.SimpleTokenizer">SimpleTokenizer</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerME">TokenizerME</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerTrainer">TokenizerTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerMEEvaluator">TokenizerMEEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerCrossValidator">TokenizerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerConverter">TokenizerConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.DictionaryDetokenizer">DictionaryDetokenizer</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.sentdetect">Sentdetect</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetector">SentenceDetector</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorTrainer">SentenceDetectorTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorEvaluator">SentenceDetectorEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorCrossValidator">SentenceDetectorCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorConverter">SentenceDetectorConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.namefind">Namefind</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinder">TokenNameFinder</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderTrainer">TokenNameFinderTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderEvaluator">TokenNameFinderEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderCrossValidator">TokenNameFinderCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderConverter">TokenNameFinderConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.CensusDictionaryCreator">CensusDictionaryCreator</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.postag">Postag</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.postag.POSTagger">POSTagger</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerTrainer">POSTaggerTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerEvaluator">POSTaggerEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerCrossValidator">POSTaggerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerConverter">POSTaggerConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.lemmatizer">Lemmatizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerME">LemmatizerME</a></span></dt><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerTrainerME">LemmatizerTrainerME</a></span></dt><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerEvaluator">LemmatizerEvaluator</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.chunker">Chunker</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.chunker.ChunkerME">ChunkerME</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerTrainerME">ChunkerTrainerME</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerEvaluator">ChunkerEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerCrossValidator">ChunkerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerConverter">ChunkerConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.parser">Parser</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.parser.Parser">Parser</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserTrainer">ParserTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserEvaluator">ParserEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserConverter">ParserConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.BuildModelUpdater">BuildModelUpdater</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.CheckModelUpdater">CheckModelUpdater</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.TaggerModelReplacer">TaggerModelReplacer</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.entitylinker">Entitylinker</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.entitylinker.EntityLinker">EntityLinker</a></span></dt></dl></dd></dl></dd></dl></div><div class="list-of-tables"><p><b>List of Tables</b></p><dl><dt>4.1. <a href="#d4e278">Generator elements</a></dt></dl></div>
	

	<div class="chapter" title="Chapter&nbsp;1.&nbsp;Introduction"><div class="titlepage"><div><div><h2 class="title"><a name="opennlp"></a>Chapter&nbsp;1.&nbsp;Introduction</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#intro.description">Description</a></span></dt><dt><span class="section"><a href="#intro.general.library.structure">General Library Structure</a></span></dt><dt><span class="section"><a href="#intro.api">Application Program Interface (API). Generic Example</a></span></dt><dt><span class="section"><a href="#intro.cli">Command line interface (CLI)</a></span></dt><dd><dl><dt><span class="section"><a href="#intro.cli.description">Description</a></span></dt><dt><span class="section"><a href="#intro.cli.toolslist">List of tools</a></span></dt><dt><span class="section"><a href="#intro.cli.setup">Setting up</a></span></dt><dt><span class="section"><a href="#intro.cli.generic">Generic Example</a></span></dt></dl></dd></dl></div>

    <div class="section" title="Description"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="intro.description"></a>Description</h2></div></div></div>
        
        <p>
        The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text.
        It supports the most common NLP tasks, such as tokenization, sentence segmentation,
        part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution.
        These tasks are usually required to build more advanced text processing services.
        OpenNLP also included maximum entropy and perceptron based machine learning.
        </p>

        <p>
        The goal of the OpenNLP project will be to create a mature toolkit for the abovementioned tasks.
        An additional goal is to provide a large number of pre-built models for a variety of languages, as
        well as the annotated text resources that those models are derived from.
        </p>
    </div>

    <div class="section" title="General Library Structure"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="intro.general.library.structure"></a>General Library Structure</h2></div></div></div>
        
        <p>The Apache OpenNLP library contains several components, enabling one to build
            a full natural language processing pipeline. These components
            include: sentence detector, tokenizer,
            name finder, document categorizer, part-of-speech tagger, chunker, parser,
            coreference resolution. Components contain parts which enable one to execute the
            respective natural language processing task, to train a model and often also to evaluate a
            model. Each of these facilities is accessible via its application program
            interface (API). In addition, a command line interface (CLI) is provided for convenience
            of experiments and training.
        </p>
    </div>

    <div class="section" title="Application Program Interface (API). Generic Example"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="intro.api"></a>Application Program Interface (API). Generic Example</h2></div></div></div>
        
        <p>
            OpenNLP components have similar APIs. Normally, to execute a task,
            one should provide a model and an input.
        </p>
        <p>
            A model is usually loaded by providing a FileInputStream with a model to a
            constructor of the model class:
            </p><pre class="programlisting">
                    
InputStream modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"lang-model-name.bin"</i></b>);

<b class="hl-keyword">try</b> {
  SomeModel model = <b class="hl-keyword">new</b> SomeModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">//handle the exception</i>
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (null != modelIn) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
            </pre><p>
        </p>
        <p>
        After the model is loaded the tool itself can be instantiated.
        </p><pre class="programlisting">
                
ToolName toolName = <b class="hl-keyword">new</b> ToolName(model);
        </pre><p>
        After the tool is instantiated, the processing task can be executed. The input and the
        output formats are specific to the tool, but often the output is an array of String,
        and the input is a String or an array of String.
        </p><pre class="programlisting">
                
String output[] = toolName.executeTask(<b class="hl-string"><i style="color:red">"This is a sample text."</i></b>);
        </pre><p>
        </p>
    </div>

    <div class="section" title="Command line interface (CLI)"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="intro.cli"></a>Command line interface (CLI)</h2></div></div></div>
        
        <div class="section" title="Description"><div class="titlepage"><div><div><h3 class="title"><a name="intro.cli.description"></a>Description</h3></div></div></div>
            
            <p>
                OpenNLP provides a command line script, serving as a unique entry point to all
                included tools. The script is located in the bin directory of OpenNLP binary
                distribution. Included are versions for Windows: opennlp.bat and Linux or
                compatible systems: opennlp.
            </p>
        </div>
        
        <div class="section" title="List of tools"><div class="titlepage"><div><div><h3 class="title"><a name="intro.cli.toolslist"></a>List of tools</h3></div></div></div>
            
            <p>
               	The list of command line tools for Apache OpenNLP 1.7.1,
               	as well as a description of its arguments, is available at section <a class="xref" href="#tools.cli" title="Chapter&nbsp;16.&nbsp;The Command Line Interface">Chapter&nbsp;16, <i>The Command Line Interface</i></a>.
            </p>
        </div>

        <div class="section" title="Setting up"><div class="titlepage"><div><div><h3 class="title"><a name="intro.cli.setup"></a>Setting up</h3></div></div></div>
            
            <p>
                OpenNLP script uses JAVA_CMD and JAVA_HOME variables to determine which command to
                use to execute Java virtual machine.
            </p>
            <p>
                OpenNLP script uses OPENNLP_HOME variable to determine the location of the binary
                distribution of OpenNLP. It is recommended to point this variable to the binary
                distribution of current OpenNLP version and update PATH variable to include
                $OPENNLP_HOME/bin or %OPENNLP_HOME%\bin.
            </p>
            <p>
                Such configuration allows calling OpenNLP conveniently. Examples below
                suppose this configuration has been done.
            </p>
        </div>

        <div class="section" title="Generic Example"><div class="titlepage"><div><div><h3 class="title"><a name="intro.cli.generic"></a>Generic Example</h3></div></div></div>
            

            <p>
                Apache OpenNLP provides a common command line script to access all its tools:
                </p><pre class="screen">
                
$ opennlp
                 </pre><p>
                This script prints current version of the library and lists all available tools:
                </p><pre class="screen">
                
OpenNLP &lt;VERSION&gt;. Usage: opennlp TOOL
where TOOL is one of:
  Doccat                            learnable document categorizer
  DoccatTrainer                     trainer for the learnable document categorizer
  DoccatConverter                   converts leipzig data format to native OpenNLP format
  DictionaryBuilder                 builds a new dictionary
  SimpleTokenizer                   character class tokenizer
  TokenizerME                       learnable tokenizer
  TokenizerTrainer                  trainer for the learnable tokenizer
  TokenizerMEEvaluator              evaluator for the learnable tokenizer
  TokenizerCrossValidator           K-fold cross validator for the learnable tokenizer
  TokenizerConverter                converts foreign data formats (namefinder,conllx,pos) to native OpenNLP format
  DictionaryDetokenizer
  SentenceDetector                  learnable sentence detector
  SentenceDetectorTrainer           trainer for the learnable sentence detector
  SentenceDetectorEvaluator         evaluator for the learnable sentence detector
  SentenceDetectorCrossValidator    K-fold cross validator for the learnable sentence detector
  SentenceDetectorConverter         converts foreign data formats (namefinder,conllx,pos) to native OpenNLP format
  TokenNameFinder                   learnable name finder
  TokenNameFinderTrainer            trainer for the learnable name finder
  TokenNameFinderEvaluator          Measures the performance of the NameFinder model with the reference data
  TokenNameFinderCrossValidator     K-fold cross validator for the learnable Name Finder
  TokenNameFinderConverter          converts foreign data formats (bionlp2004,conll03,conll02,ad) to native OpenNLP format
  CensusDictionaryCreator           Converts 1990 US Census names into a dictionary
  POSTagger                         learnable part of speech tagger
  POSTaggerTrainer                  trains a model for the part-of-speech tagger
  POSTaggerEvaluator                Measures the performance of the POS tagger model with the reference data
  POSTaggerCrossValidator           K-fold cross validator for the learnable POS tagger
  POSTaggerConverter                converts conllx data format to native OpenNLP format
  ChunkerME                         learnable chunker
  ChunkerTrainerME                  trainer for the learnable chunker
  ChunkerEvaluator                  Measures the performance of the Chunker model with the reference data
  ChunkerCrossValidator             K-fold cross validator for the chunker
  ChunkerConverter                  converts ad data format to native OpenNLP format
  Parser                            performs full syntactic parsing
  ParserTrainer                     trains the learnable parser
  ParserEvaluator					Measures the performance of the Parser model with the reference data
  BuildModelUpdater                 trains and updates the build model in a parser model
  CheckModelUpdater                 trains and updates the check model in a parser model
  TaggerModelReplacer               replaces the tagger model in a parser model
All tools print help when invoked with help parameter
Example: opennlp SimpleTokenizer help

                </pre><p>
            </p>
            <p>OpenNLP tools have similar command line structure and options. To discover tool
                options, run it with no parameters:
                </p><pre class="screen">
                
$ opennlp ToolName
                 </pre><p>
                The tool will output two blocks of help.
            </p>
            <p>
                The first block describes the general structure of this tool command line:
                </p><pre class="screen">
                
Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] ...  -model modelFile ...
                </pre><p>
                The general structure of this tool command line includes the obligatory tool name
                (TokenizerTrainer), the optional format parameters ([.namefinder|.conllx|.pos]),
                the optional parameters ([-abbDict path] ...), and the obligatory parameters
                (-model modelFile ...).
            </p>
            <p>
                The format parameters enable direct processing of non-native data without conversion.
                Each format might have its own parameters, which are displayed if the tool is
                executed without or with help parameter:
                </p><pre class="screen">
                
$ opennlp TokenizerTrainer.conllx help
                </pre><p>
                </p><pre class="screen">
                
Usage: opennlp TokenizerTrainer.conllx [-abbDict path] [-alphaNumOpt isAlphaNumOpt] ...

Arguments description:
        -abbDict path
                abbreviation dictionary in XML format.
        ...
                </pre><p>
                To switch the tool to a specific format, add a dot and the format name after
                the tool name:
                </p><pre class="screen">
                
$ opennlp TokenizerTrainer.conllx -model en-pos.bin ...
                </pre><p>
            </p>
            <p>
                The second block of the help message describes the individual arguments:
                </p><pre class="screen">
                
Arguments description:
        -type maxent|perceptron|perceptron_sequence
                The type of the token name finder model. One of maxent|perceptron|perceptron_sequence.
        -dict dictionaryPath
                The XML tag dictionary file
        ...
                </pre><p>
            </p>
            <p>
                Most tools for processing need to be provided at least a model:
                </p><pre class="screen">
                
$ opennlp ToolName lang-model-name.bin
                 </pre><p>
                When tool is executed this way, the model is loaded and the tool is waiting for
                the input from standard input. This input is processed and printed to standard
                output.
            </p>
            <p>Alternative, or one should say, most commonly used way is to use console input and
                output redirection options to provide also an input and an output files:
                </p><pre class="screen">
            
$ opennlp ToolName lang-model-name.bin &lt; input.txt &gt; output.txt
                </pre><p>
            </p>
            <p>
                Most tools for model training need to be provided first a model name,
                optionally some training options (such as model type, number of iterations),
                and then the data.
            </p>
            <p>
                A model name is just a file name.
            </p>
            <p>
                Training options often include number of iterations, cutoff,
                abbreviations dictionary or something else. Sometimes it is possible to provide these
                options via training options file. In this case these options are ignored and the
                ones from the file are used.
            </p>
            <p>
                For the data one has to specify the location of the data (filename) and often
                language and encoding.
            </p>
            <p>
                A generic example of a command line to launch a tool trainer might be:
                </p><pre class="screen">
                
$ opennlp ToolNameTrainer -model en-model-name.bin -lang en -data input.train -encoding UTF-8
                 </pre><p>
                or with a format:
                </p><pre class="screen">
                
$ opennlp ToolNameTrainer.conll03 -model en-model-name.bin -lang en -data input.train \
                                  -types per -encoding UTF-8
                 </pre><p>
            </p>
            <p>Most tools for model evaluation are similar to those for task execution, and
                need to be provided fist a model name, optionally some evaluation options (such
                as whether to print misclassified samples), and then the test data. A generic
                example of a command line to launch an evaluation tool might be:
                </p><pre class="screen">
                
$ opennlp ToolNameEvaluator -model en-model-name.bin -lang en -data input.test -encoding UTF-8
                 </pre><p>
            </p>
        </div>
    </div>

</div>
	<div class="chapter" title="Chapter&nbsp;2.&nbsp;Sentence Detector"><div class="titlepage"><div><div><h2 class="title"><a name="tools.sentdetect"></a>Chapter&nbsp;2.&nbsp;Sentence Detector</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.sentdetect.detection">Sentence Detection</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.detection.cmdline">Sentence Detection Tool</a></span></dt><dt><span class="section"><a href="#tools.sentdetect.detection.api">Sentence Detection API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.sentdetect.training">Sentence Detector Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.sentdetect.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.sentdetect.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.sentdetect.eval.tool">Evaluation Tool</a></span></dt></dl></dd></dl></div>

	
	<div class="section" title="Sentence Detection"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.sentdetect.detection"></a>Sentence Detection</h2></div></div></div>
		
		<p>
		The OpenNLP Sentence Detector can detect that a punctuation character 
		marks the end of a sentence or not. In this sense a sentence is defined 
		as the longest white space trimmed character sequence between two punctuation
		marks. The first and last sentence make an exception to this rule. The first 
		non whitespace character is assumed to be the begin of a sentence, and the 
		last non whitespace character is assumed to be a sentence end.
		The sample text below should be segmented into its sentences.
		</p><pre class="screen">
				
Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29. Mr. Vinken is
chairman of Elsevier N.V., the Dutch publishing group. Rudolph Agnew, 55 years
old and former chairman of Consolidated Gold Fields PLC, was named a director of this
British industrial conglomerate.
		</pre><p>
		After detecting the sentence boundaries each sentence is written in its own line.
		</p><pre class="screen">
				
Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group.
Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC,
    was named a director of this British industrial conglomerate.
		</pre><p>
		Usually Sentence Detection is done before the text is tokenized and that's the way the pre-trained models on the web site are trained,
		but it is also possible to perform tokenization first and let the Sentence Detector process the already tokenized text.
		The OpenNLP Sentence Detector cannot identify sentence boundaries based on the contents of the sentence. A prominent example is the first sentence in an article where the title is mistakenly identified to be the first part of the first sentence.
		Most components in OpenNLP expect input which is segmented into sentences.
		</p>
		
		<div class="section" title="Sentence Detection Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.sentdetect.detection.cmdline"></a>Sentence Detection Tool</h3></div></div></div>
		
		<p>
		The easiest way to try out the Sentence Detector is the command line tool. The tool is only intended for demonstration and testing.
		Download the english sentence detector model and start the Sentence Detector Tool with this command:
        </p><pre class="screen">
        
$ opennlp SentenceDetector en-sent.bin
		</pre><p>
		Just copy the sample text from above to the console. The Sentence Detector will read it and echo one sentence per line to the console.
		Usually the input is read from a file and the output is redirected to another file. This can be achieved with the following command.
		</p><pre class="screen">
				
$ opennlp SentenceDetector en-sent.bin &lt; input.txt &gt; output.txt
		</pre><p>
		For the english sentence model from the website the input text should not be tokenized.
		</p>
		</div>
		<div class="section" title="Sentence Detection API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.sentdetect.detection.api"></a>Sentence Detection API</h3></div></div></div>
		
		<p>
		The Sentence Detector can be easily integrated into an application via its API.
		To instantiate the Sentence Detector the sentence model must be loaded first.
		</p><pre class="programlisting">
				
InputStream modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-sent.bin"</i></b>);

<b class="hl-keyword">try</b> {
  SentenceModel model = <b class="hl-keyword">new</b> SentenceModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
		</pre><p>
		After the model is loaded the SentenceDetectorME can be instantiated.
		</p><pre class="programlisting">
				
SentenceDetectorME sentenceDetector = <b class="hl-keyword">new</b> SentenceDetectorME(model);
		</pre><p>
		The Sentence Detector can output an array of Strings, where each String is one sentence.
				</p><pre class="programlisting">
				
String sentences[] = sentenceDetector.sentDetect(<b class="hl-string"><i style="color:red">"  First sentence. Second sentence. "</i></b>);
		</pre><p>
		The result array now contains two entries. The first String is "First sentence." and the
        second String is "Second sentence." The whitespace before, between and after the input String is removed.
		The API also offers a method which simply returns the span of the sentence in the input string.
		</p><pre class="programlisting">
				
Span sentences[] = sentenceDetector.sentPosDetect(<b class="hl-string"><i style="color:red">"  First sentence. Second sentence. "</i></b>);
		</pre><p>
		The result array again contains two entries. The first span beings at index 2 and ends at
            17. The second span begins at 18 and ends at 34. The utility method Span.getCoveredText can be used to create a substring which only covers the chars in the span.
		</p>
		</div>
	</div>
	<div class="section" title="Sentence Detector Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.sentdetect.training"></a>Sentence Detector Training</h2></div></div></div>
		
		<p></p>
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.sentdetect.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
		OpenNLP has a command line tool which is used to train the models available from the model
		download page on various corpora. The data must be converted to the OpenNLP Sentence Detector
		training format. Which is one sentence per line. An empty line indicates a document boundary.
		In case the document boundary is unknown, its recommended to have an empty line every few ten
		sentences. Exactly like the output in the sample above.
		Usage of the tool:
		</p><pre class="screen">
				
$ opennlp SentenceDetectorTrainer
Usage: opennlp SentenceDetectorTrainer[.namefinder|.conllx|.pos] [-abbDict path] \
               [-params paramsFile] [-iterations num] [-cutoff num] -model modelFile \
               -lang language -data sampleData [-encoding charsetName]

Arguments description:
        -abbDict path
                abbreviation dictionary in XML format.
        -params paramsFile
                training parameters file.
        -iterations num
                number of training iterations, ignored if -params is used.
        -cutoff num
                minimal number of times a feature must be seen, ignored if -params is used.
        -model modelFile
                output model file.
        -lang language
                language which is being processed.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
	</pre><p>
		To train an English sentence detector use the following command:
        </p><pre class="screen">
				
$ opennlp SentenceDetectorTrainer -model en-sent.bin -lang en -data en-sent.train -encoding UTF-8
                        
        </pre><p>
            It should produce the following output:
            </p><pre class="screen">
                
Indexing events using cutoff of 5

	Computing event counts...  done. 4883 events
	Indexing...  done.
Sorting and merging events... done. Reduced 4883 events to 2945.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 2945
	    Number of Outcomes: 2
	  Number of Predicates: 467
...done.
Computing model parameters...
Performing 100 iterations.
  1:  .. loglikelihood=-3384.6376826743144	0.38951464263772273
  2:  .. loglikelihood=-2191.9266688597672	0.9397911120212984
  3:  .. loglikelihood=-1645.8640771555981	0.9643661683391358
  4:  .. loglikelihood=-1340.386303774519	0.9739913987302887
  5:  .. loglikelihood=-1148.4141548519624	0.9748105672742167

 ...&lt;skipping a bunch of iterations&gt;...

 95:  .. loglikelihood=-288.25556805874436	0.9834118369854598
 96:  .. loglikelihood=-287.2283680343481	0.9834118369854598
 97:  .. loglikelihood=-286.2174830344526	0.9834118369854598
 98:  .. loglikelihood=-285.222486981048	0.9834118369854598
 99:  .. loglikelihood=-284.24296917223916	0.9834118369854598
100:  .. loglikelihood=-283.2785335773966	0.9834118369854598
Wrote sentence detector model.
Path: en-sent.bin

		</pre><p>
		</p>
		</div>
		<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.sentdetect.training.api"></a>Training API</h3></div></div></div>
		
		<p>
		The Sentence Detector also offers an API to train a new sentence detection model.
		Basically three steps are necessary to train it:
		</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>The application must open a sample data stream</p>
				</li><li class="listitem">
					<p>Call the SentenceDetectorME.train method</p>
				</li><li class="listitem">
					<p>Save the SentenceModel to a file or directly use it</p>
				</li></ul></div><p>
			The following sample code illustrates these steps:
					</p><pre class="programlisting">
				
Charset charset = Charset.forName(<b class="hl-string"><i style="color:red">"UTF-8"</i></b>);				
ObjectStream&lt;String&gt; lineStream =
  <b class="hl-keyword">new</b> PlainTextByLineStream(<b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-sent.train"</i></b>), charset);
ObjectStream&lt;SentenceSample&gt; sampleStream = <b class="hl-keyword">new</b> SentenceSampleStream(lineStream);

SentenceModel model;

<b class="hl-keyword">try</b> {
  model = SentenceDetectorME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, sampleStream, true, null, TrainingParameters.defaultParams());
}
<b class="hl-keyword">finally</b> {
  sampleStream.close();
}

OutputStream modelOut = null;
<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null) 
     modelOut.close();      
}
		</pre><p>
		</p>
		</div>
	</div>
	<div class="section" title="Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.sentdetect.eval"></a>Evaluation</h2></div></div></div>
		
		<p>
		</p>
		<div class="section" title="Evaluation Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.sentdetect.eval.tool"></a>Evaluation Tool</h3></div></div></div>
			
			<p>
                The command shows how the evaluator tool can be run:
                </p><pre class="screen">
				
$ opennlp SentenceDetectorEvaluator -model en-sent.bin -data en-sent.eval -encoding UTF-8

Loading model ... done
Evaluating ... done

Precision: 0.9465737514518002
Recall: 0.9095982142857143
F-Measure: 0.9277177006260672
                </pre><p>
                The en-sent.eval file has the same format as the training data.
			</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;3.&nbsp;Tokenizer"><div class="titlepage"><div><div><h2 class="title"><a name="tools.tokenizer"></a>Chapter&nbsp;3.&nbsp;Tokenizer</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.tokenizer.introduction">Tokenization</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.cmdline">Tokenizer Tools</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.api">Tokenizer API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.tokenizer.training">Tokenizer Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.tokenizer.detokenizing">Detokenizing</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.tokenizer.detokenizing.api">Detokenizing API</a></span></dt><dt><span class="section"><a href="#tools.tokenizer.detokenizing.dict">Detokenizer Dictionary</a></span></dt></dl></dd></dl></div>

	
	<div class="section" title="Tokenization"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.tokenizer.introduction"></a>Tokenization</h2></div></div></div>
		
		<p>
			The OpenNLP Tokenizers segment an input character sequence into
			tokens. Tokens are usually
			words, punctuation, numbers, etc.

			</p><pre class="screen">
			
Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group.
Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields
    PLC, was named a director of this British industrial conglomerate.
			
		    </pre><p>

			The following result shows the individual tokens in a whitespace
			separated representation.

			</p><pre class="screen">
			
Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
Mr. Vinken is chairman of Elsevier N.V. , the Dutch publishing group .
Rudolph Agnew , 55 years old and former chairman of Consolidated Gold Fields PLC ,
    was named a nonexecutive director of this British industrial conglomerate . 
A form of asbestos once used to make Kent cigarette filters has caused a high
    percentage of cancer deaths among a group of workers exposed to it more than 30 years ago ,
    researchers reported . 
			
		 	</pre><p>

			OpenNLP offers multiple tokenizer implementations:
			</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>Whitespace Tokenizer - A whitespace tokenizer, non whitespace
						sequences are identified as tokens</p>
				</li><li class="listitem">
					<p>Simple Tokenizer - A character class tokenizer, sequences of
						the same character class are tokens</p>
				</li><li class="listitem">
					<p>Learnable Tokenizer - A maximum entropy tokenizer, detects
						token boundaries based on probability model</p>
				</li></ul></div><p>

			Most part-of-speech taggers, parsers and so on, work with text
			tokenized in this manner. It is important to ensure that your
			tokenizer
			produces tokens of the type expected by your later text
			processing
			components.
		</p>

		<p>
			With OpenNLP (as with many systems), tokenization is a two-stage
			process:
			first, sentence boundaries are identified, then tokens within
			each
			sentence are identified.
		</p>
	
	<div class="section" title="Tokenizer Tools"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.cmdline"></a>Tokenizer Tools</h3></div></div></div>
		
		<p>The easiest way to try out the tokenizers are the command line
			tools. The tools are only intended for demonstration and testing.
		</p>
		<p>There are two tools, one for the Simple Tokenizer and one for
			the learnable tokenizer. A command line tool the for the Whitespace
			Tokenizer does not exist, because the whitespace separated output
			would be identical to the input.</p>
		<p>
			The following command shows how to use the Simple Tokenizer Tool.

			</p><pre class="screen">
			
$ opennlp SimpleTokenizer
		    </pre><p>
			To use the learnable tokenizer download the english token model from
			our website.
			</p><pre class="screen">
			
$ opennlp TokenizerME en-token.bin
		    </pre><p>
			To test the tokenizer copy the sample from above to the console. The
			whitespace separated tokens will be written back to the
			console.
		</p>
		<p>
			Usually the input is read from a file and written to a file.
			</p><pre class="screen">
			
$ opennlp TokenizerME en-token.bin &lt; article.txt &gt; article-tokenized.txt
		    </pre><p>
			It can be done in the same way for the Simple Tokenizer.
		</p>
		<p>
			Since most text comes truly raw and doesn't have sentence boundaries
			and such, its possible to create a pipe which first performs sentence
			boundary detection and tokenization. The following sample illustrates
			that.
			</p><pre class="screen">
			
$ opennlp SentenceDetector sentdetect.model &lt; article.txt | opennlp TokenizerME tokenize.model | more
Loading model ... Loading model ... done
done
Showa Shell gained 20 to 1,570 and Mitsubishi Oil rose 50 to 1,500.
Sumitomo Metal Mining fell five yen to 692 and Nippon Mining added 15 to 960 .
Among other winners Wednesday was Nippon Shokubai , which was up 80 at 2,410 .
Marubeni advanced 11 to 890 .
London share prices were bolstered largely by continued gains on Wall Street and technical 
    factors affecting demand for London 's blue-chip stocks .
...etc...
		 </pre><p>
			Of course this is all on the command line. Many people use the models
			directly in their Java code by creating SentenceDetector and
			Tokenizer objects and calling their methods as appropriate. The
			following section will explain how the Tokenizers can be used
			directly from java.
		</p>
	</div>

	<div class="section" title="Tokenizer API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.api"></a>Tokenizer API</h3></div></div></div>
		
		<p>
			The Tokenizers can be integrated into an application by the defined
			API.
			The shared instance of the WhitespaceTokenizer can be retrieved from a
			static field WhitespaceTokenizer.INSTANCE. The shared instance of the
			SimpleTokenizer can be retrieved in the same way from
			SimpleTokenizer.INSTANCE.
			To instantiate the TokenizerME (the learnable tokenizer) a Token Model
			must be created first. The following code sample shows how a model
			can be loaded.
			</p><pre class="programlisting">
			
InputStream modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-token.bin"</i></b>);

<b class="hl-keyword">try</b> {
  TokenizerModel model = <b class="hl-keyword">new</b> TokenizerModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
		 </pre><p>
			After the model is loaded the TokenizerME can be instantiated.
			</p><pre class="programlisting">
			
Tokenizer tokenizer = <b class="hl-keyword">new</b> TokenizerME(model);
		 </pre><p>
			The tokenizer offers two tokenize methods, both expect an input
			String object which contains the untokenized text. If possible it
			should be a sentence, but depending on the training of the learnable
			tokenizer this is not required. The first returns an array of
			Strings, where each String is one token.
			</p><pre class="programlisting">
			
String tokens[] = tokenizer.tokenize(<b class="hl-string"><i style="color:red">"An input sample sentence."</i></b>);
		 </pre><p>
			The output will be an array with these tokens.
			</p><pre class="programlisting">
			
"An", "input", "sample", "sentence", "."
		 </pre><p>
			The second method, tokenizePos returns an array of Spans, each Span
			contain the begin and end character offsets of the token in the input
			String.
			</p><pre class="programlisting">
			
Span tokenSpans[] = tokenizer.tokenizePos(<b class="hl-string"><i style="color:red">"An input sample sentence."</i></b>);		
			</pre><p>
			The tokenSpans array now contain 5 elements. To get the text for one
			span call Span.getCoveredText which takes a span and the input text.

			The TokenizerME is able to output the probabilities for the detected
			tokens. The getTokenProbabilities method must be called directly
			after one of the tokenize methods was called.
			</p><pre class="programlisting">
			
TokenizerME tokenizer = ...

String tokens[] = tokenizer.tokenize(...);
<b class="hl-keyword">double</b> tokenProbs[] = tokenizer.getTokenProbabilities();
			</pre><p>
			The tokenProbs array now contains one double value per token, the
			value is between 0 and 1, where 1 is the highest possible probability
			and 0 the lowest possible probability.
		</p>
	</div>
	</div>
	
	<div class="section" title="Tokenizer Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.tokenizer.training"></a>Tokenizer Training</h2></div></div></div>
		
			
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.training.tool"></a>Training Tool</h3></div></div></div>
			
			<p>
				OpenNLP has a command line tool which is used to train the models
				available from the model download page on various corpora. The data
				can be converted to the OpenNLP Tokenizer training format or used directly.
                The OpenNLP format contains one sentence per line. Tokens are either separated by a
                whitespace or by a special &lt;SPLIT&gt; tag.
				
				The following sample shows the sample from above in the correct format.
				</p><pre class="screen">
			    
Pierre Vinken&lt;SPLIT&gt;, 61 years old&lt;SPLIT&gt;, will join the board as a nonexecutive director Nov. 29&lt;SPLIT&gt;.
Mr. Vinken is chairman of Elsevier N.V.&lt;SPLIT&gt;, the Dutch publishing group&lt;SPLIT&gt;.
Rudolph Agnew&lt;SPLIT&gt;, 55 years old and former chairman of Consolidated Gold Fields PLC&lt;SPLIT&gt;,
    was named a nonexecutive director of this British industrial conglomerate&lt;SPLIT&gt;.
			    </pre><p>
			    Usage of the tool:
			    </p><pre class="screen">
			    
$ opennlp TokenizerTrainer
Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] \
                [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] [-iterations num] \
                [-cutoff num] -model modelFile -lang language -data sampleData \
                [-encoding charsetName]

Arguments description:
        -abbDict path
                abbreviation dictionary in XML format.
        -alphaNumOpt isAlphaNumOpt
                Optimization flag to skip alpha numeric tokens for further tokenization
        -params paramsFile
                training parameters file.
        -iterations num
                number of training iterations, ignored if -params is used.
        -cutoff num
                minimal number of times a feature must be seen, ignored if -params is used.
        -model modelFile
                output model file.
        -lang language
                language which is being processed.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
                </pre><p>
				To train the english tokenizer use the following command:
				</p><pre class="screen">
			    
$ opennlp TokenizerTrainer -model en-token.bin -alphaNumOpt -lang en -data en-token.train -encoding UTF-8

Indexing events using cutoff of 5

	Computing event counts...  done. 262271 events
	Indexing...  done.
Sorting and merging events... done. Reduced 262271 events to 59060.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 59060
	    Number of Outcomes: 2
	  Number of Predicates: 15695
...done.
Computing model parameters...
Performing 100 iterations.
  1:  .. loglikelihood=-181792.40419263614	0.9614292087192255
  2:  .. loglikelihood=-34208.094253153664	0.9629238459456059
  3:  .. loglikelihood=-18784.123872910015	0.9729211388220581
  4:  .. loglikelihood=-13246.88162585859	0.9856103038460219
  5:  .. loglikelihood=-10209.262670265718	0.9894422181636552

 ...&lt;skipping a bunch of iterations&gt;...

 95:  .. loglikelihood=-769.2107474529454	0.999511955191386
 96:  .. loglikelihood=-763.8891914534009	0.999511955191386
 97:  .. loglikelihood=-758.6685383254891	0.9995157680414533
 98:  .. loglikelihood=-753.5458314695236	0.9995157680414533
 99:  .. loglikelihood=-748.5182305519613	0.9995157680414533
100:  .. loglikelihood=-743.5830058068038	0.9995157680414533
Wrote tokenizer model.
Path: en-token.bin
				</pre><p>
			</p>
		</div>
		<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.training.api"></a>Training API</h3></div></div></div>
			
            <p>
                The Tokenizer offers an API to train a new tokenization model. Basically three steps
                are necessary to train it:
                </p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
                        <p>The application must open a sample data stream</p>
                    </li><li class="listitem">
                        <p>Call the TokenizerME.train method</p>
                    </li><li class="listitem">
                        <p>Save the TokenizerModel to a file or directly use it</p>
                    </li></ul></div><p>
                The following sample code illustrates these steps:
                </p><pre class="programlisting">
                    
Charset charset = Charset.forName(<b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
ObjectStream&lt;String&gt; lineStream = <b class="hl-keyword">new</b> PlainTextByLineStream(<b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-sent.train"</i></b>),
    charset);
ObjectStream&lt;TokenSample&gt; sampleStream = <b class="hl-keyword">new</b> TokenSampleStream(lineStream);

TokenizerModel model;

<b class="hl-keyword">try</b> {
  model = TokenizerME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, sampleStream, true, TrainingParameters.defaultParams());
}
<b class="hl-keyword">finally</b> {
  sampleStream.close();
}

OutputStream modelOut = null;
<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null)
     modelOut.close();
}
                </pre><p>
            </p>
		</div>
	</div>
	
	<div class="section" title="Detokenizing"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.tokenizer.detokenizing"></a>Detokenizing</h2></div></div></div>
		
		<p>
		Detokenizing is simple the opposite of tokenization, the original non-tokenized string should
		be constructed out of a token sequence. The OpenNLP implementation was created to undo the tokenization
		of training data for the tokenizer. It can also be used to undo the tokenization of such a trained
		tokenizer. The implementation is strictly rule based and defines how tokens should be attached
		to a sentence wise character sequence.
		</p>
		<p>
		The rule dictionary assign to every token an operation which describes how it should be attached
		to one continuous character sequence.
		</p>
		<p>
		The following rules can be assigned to a token:
		</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
				<p>MERGE_TO_LEFT - Merges the token to the left side.</p>
			</li><li class="listitem">
				<p>MERGE_TO_RIGHT - Merges the token to the right side.</p>
			</li><li class="listitem">
				<p>RIGHT_LEFT_MATCHING - Merges the token to the right side on first occurrence
				and to the left side on second occurrence.</p>
			</li></ul></div><p>

		The following sample will illustrate how the detokenizer with a small
		rule dictionary (illustration format, not the xml data format):
		</p><pre class="programlisting">
			
. MERGE_TO_LEFT
" RIGHT_LEFT_MATCHING		
		</pre><p>
		The dictionary should be used to de-tokenize the following whitespace tokenized sentence:
		</p><pre class="programlisting">
			
He said " This is a test " .		
		</pre><p>
		The tokens would get these tags based on the dictionary:
		</p><pre class="programlisting">
			
He -&gt; NO_OPERATION
said -&gt; NO_OPERATION
" -&gt; MERGE_TO_RIGHT
This -&gt; NO_OPERATION
is -&gt; NO_OPERATION
a -&gt; NO_OPERATION
test -&gt; NO_OPERATION
" -&gt; MERGE_TO_LEFT
. -&gt; MERGE_TO_LEFT		
			</pre><p>
			That will result in the following character sequence:
		</p><pre class="programlisting">
			
He said "This is a test".		
		</pre><p>
		TODO: Add documentation about the dictionary format and how to use the API. Contributions are welcome.
		</p>
		<div class="section" title="Detokenizing API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.detokenizing.api"></a>Detokenizing API</h3></div></div></div>
			
			<p>TODO: Write documentation about the detokenizer api. Any contributions
are very welcome. If you want to contribute please contact us on the mailing list
or comment on the jira issue <a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-216" target="_top">OPENNLP-216</a>.</p>
		</div>
		<div class="section" title="Detokenizer Dictionary"><div class="titlepage"><div><div><h3 class="title"><a name="tools.tokenizer.detokenizing.dict"></a>Detokenizer Dictionary</h3></div></div></div>
			
			<p>TODO: Write documentation about the detokenizer dictionary. Any contributions
are very welcome. If you want to contribute please contact us on the mailing list
or comment on the jira issue <a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-217" target="_top">OPENNLP-217</a>.</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;4.&nbsp;Name Finder"><div class="titlepage"><div><div><h2 class="title"><a name="tools.namefind"></a>Chapter&nbsp;4.&nbsp;Name Finder</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.namefind.recognition">Named Entity Recognition</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.recognition.cmdline">Name Finder Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.recognition.api">Name Finder API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.training">Name Finder Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.training.api">Training API</a></span></dt><dt><span class="section"><a href="#tools.namefind.training.featuregen">Custom Feature Generation</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.namefind.eval.tool">Evaluation Tool</a></span></dt><dt><span class="section"><a href="#tools.namefind.eval.api">Evaluation API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.namefind.annotation_guides">Named Entity Annotation Guidelines</a></span></dt></dl></div>

	
	<div class="section" title="Named Entity Recognition"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.namefind.recognition"></a>Named Entity Recognition</h2></div></div></div>
		
		<p>
			The Name Finder can detect named entities and numbers in text. To be able to
			detect entities the Name Finder needs a model. The model is dependent on the
			language and entity type it was trained for. The OpenNLP projects offers a number
			of pre-trained name finder models which are trained on various freely available corpora.
			They can be downloaded at our model download page. To find names in raw text the text
			must be segmented into tokens and sentences. A detailed description is given in the
			sentence detector and tokenizer tutorial. It is important that the tokenization for
			the training data and the input text is identical.
		</p>
	
	<div class="section" title="Name Finder Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.recognition.cmdline"></a>Name Finder Tool</h3></div></div></div>
		
		<p>
			The easiest way to try out the Name Finder is the command line tool.
			The tool is only intended for demonstration and testing. Download the
			English
			person model and start the Name Finder Tool with this command:
			</p><pre class="screen">
				
$ opennlp TokenNameFinder en-ner-person.bin
			 </pre><p>
			 
			The name finder now reads a tokenized sentence per line from stdin, an empty
			line indicates a document boundary and resets the adaptive feature generators.
			Just copy this text to the terminal:
	
			</p><pre class="screen">
				
Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
Mr . Vinken is chairman of Elsevier N.V. , the Dutch publishing group .
Rudolph Agnew , 55 years old and former chairman of Consolidated Gold Fields PLC , was named
    a director of this British industrial conglomerate .
			 </pre><p>
			 the name finder will now output the text with markup for person names:
			</p><pre class="screen">
				
&lt;START:person&gt; Pierre Vinken &lt;END&gt; , 61 years old , will join the board as a nonexecutive director Nov. 29 .
Mr . &lt;START:person&gt; Vinken &lt;END&gt; is chairman of Elsevier N.V. , the Dutch publishing group .
&lt;START:person&gt; Rudolph Agnew &lt;END&gt; , 55 years old and former chairman of Consolidated Gold Fields PLC ,
    was named a director of this British industrial conglomerate .
			 </pre><p>
		</p>
	</div>
		<div class="section" title="Name Finder API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.recognition.api"></a>Name Finder API</h3></div></div></div>
		
		<p>
			To use the Name Finder in a production system it is strongly recommended to embed it
			directly into the application instead of using the command line interface.
			First the name finder model must be loaded into memory from disk or an other source.
			In the sample below it is loaded from disk.
			</p><pre class="programlisting">
				
InputStream modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-ner-person.bin"</i></b>);

<b class="hl-keyword">try</b> {
  TokenNameFinderModel model = <b class="hl-keyword">new</b> TokenNameFinderModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
			 </pre><p>
			 There is a number of reasons why the model loading can fail:
			 </p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>Issues with the underlying I/O</p>
				</li><li class="listitem">
					<p>The version of the model is not compatible with the OpenNLP version</p>
				</li><li class="listitem">
					<p>The model is loaded into the wrong component,
					for example a tokenizer model is loaded with TokenNameFinderModel class.</p>
				</li><li class="listitem">
					<p>The model content is not valid for some other reason</p>
				</li></ul></div><p>
			After the model is loaded the NameFinderME can be instantiated.
			</p><pre class="programlisting">
				
NameFinderME nameFinder = <b class="hl-keyword">new</b> NameFinderME(model);
			</pre><p>
			The initialization is now finished and the Name Finder can be used. The NameFinderME
			class is not thread safe, it must only be called from one thread. To use multiple threads
			multiple NameFinderME instances sharing the same model instance can be created.
			The input text should be segmented into documents, sentences and tokens.
			To perform entity detection an application calls the find method for every sentence in the
			document. After every document clearAdaptiveData must be called to clear the adaptive data in
			the feature generators. Not calling clearAdaptiveData can lead to a sharp drop in the detection
			rate after a few documents.
			The following code illustrates that:
			</p><pre class="programlisting">
				
<b class="hl-keyword">for</b> (String document[][] : documents) {

  <b class="hl-keyword">for</b> (String[] sentence : document) {
    Span nameSpans[] = nameFinder.find(sentence);
    <i class="hl-comment" style="color: silver">// do something with the names</i>
  }

  nameFinder.clearAdaptiveData()
}
			 </pre><p>
			 the following snippet shows a call to find
			 </p><pre class="programlisting">
				
String sentence[] = <b class="hl-keyword">new</b> String[]{
    <b class="hl-string"><i style="color:red">"Pierre"</i></b>,
    <b class="hl-string"><i style="color:red">"Vinken"</i></b>,
    <b class="hl-string"><i style="color:red">"is"</i></b>,
    <b class="hl-string"><i style="color:red">"61"</i></b>,
    <b class="hl-string"><i style="color:red">"years"</i></b>
    <b class="hl-string"><i style="color:red">"old"</i></b>,
    <b class="hl-string"><i style="color:red">"."</i></b>
    };

Span nameSpans[] = nameFinder.find(sentence);
			</pre><p>
			The nameSpans arrays contains now exactly one Span which marks the name Pierre Vinken. 
			The elements between the begin and end offsets are the name tokens. In this case the begin 
			offset is 0 and the end offset is 2. The Span object also knows the type of the entity.
			In this case it is person (defined by the model). It can be retrieved with a call to Span.getType().
			Additionally to the statistical Name Finder, OpenNLP also offers a dictionary and a regular
			expression name finder implementation.
		</p>
		<p>
			TODO: Explain how to retrieve probs from the name finder for names and for non recognized names
		</p>
	</div>
	</div>
	<div class="section" title="Name Finder Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.namefind.training"></a>Name Finder Training</h2></div></div></div>
		
		<p>
			The pre-trained models might not be available for a desired language, can not detect
			important entities or the performance is not good enough outside the news domain.
			These are the typical reason to do custom training of the name finder on a new corpus
			or on a corpus which is extended by private training data taken from the data which should be analyzed.
		</p>
		
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
			OpenNLP has a command line tool which is used to train the models available from the model
			download page on various corpora.
		</p>
		<p>
			The data can be converted to the OpenNLP name finder training format. Which is one
            sentence per line. Some other formats are available as well.
			The sentence must be tokenized and contain spans which mark the entities. Documents are separated by
			empty lines which trigger the reset of the adaptive feature generators. A training file can contain
			multiple types. If the training file contains multiple types the created model will also be able to
			detect these multiple types.
		</p>
		<p>
			Sample sentence of the data:
			</p><pre class="screen">
				
&lt;START:person&gt; Pierre Vinken &lt;END&gt; , 61 years old , will join the board as a nonexecutive director Nov. 29 .
Mr . &lt;START:person&gt; Vinken &lt;END&gt; is chairman of Elsevier N.V. , the Dutch publishing group .
			 </pre><p>
			 The training data should contain at least 15000 sentences to create a model which performs well.
			 Usage of the tool:
			</p><pre class="screen">
				
$ opennlp TokenNameFinderTrainer
Usage: opennlp TokenNameFinderTrainer[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] \
[-featuregen featuregenFile] [-nameTypes types] [-sequenceCodec codec] [-factory factoryName] \
[-resources resourcesDir] [-type modelType] [-params paramsFile] -lang language \
-model modelFile -data sampleData [-encoding charsetName]

Arguments description:
        -featuregen featuregenFile
                The feature generator descriptor file
        -nameTypes types
                name types to use for training
        -sequenceCodec codec
                sequence codec used to code name spans
        -factory factoryName
                A sub-class of TokenNameFinderFactory
        -resources resourcesDir
                The resources directory
        -type modelType
                The type of the token name finder model
        -params paramsFile
                training parameters file.
        -lang language
                language which is being processed.
        -model modelFile
                output model file.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
			 </pre><p>
			 It is now assumed that the english person name finder model should be trained from a file
			 called en-ner-person.train which is encoded as UTF-8. The following command will train
			 the name finder and write the model to en-ner-person.bin:
			 </p><pre class="screen">
				
$ opennlp TokenNameFinderTrainer -model en-ner-person.bin -lang en -data en-ner-person.train -encoding UTF-8
			 </pre><p>
The example above will train models with a pre-defined feature set. It is also possible to use the -resources parameter to generate features based on external knowledge such as those based on word representation (clustering) features. The external resources must all be placed in a resource directory which is then passed as a parameter. If this option is used it is then required to pass, via the -featuregen parameter, a XML custom feature generator which includes some of the clustering features shipped with the TokenNameFinder. Currently three formats of clustering lexicons are accepted:
			</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>Space separated two column file specifying the token and the cluster class as generated by toolkits such as <a class="ulink" href="https://code.google.com/p/word2vec/" target="_top">word2vec</a>.</p>
				</li><li class="listitem">
					<p>Space separated three column file specifying the token, clustering class and weight as such as <a class="ulink" href="https://github.com/ninjin/clark_pos_induction" target="_top">Clark's clusters</a>.</p>
				</li><li class="listitem">
					<p>Tab separated three column Brown clusters as generated by <a class="ulink" href="https://github.com/percyliang/brown-cluster" target="_top">
						Liang's toolkit</a>.</p>
				</li></ul></div><p>
			 Additionally it is possible to specify the number of iterations,
			 the cutoff and to overwrite all types in the training data with a single type. Finally, the -sequenceCodec parameter allows to specify a BIO (Begin, Inside, Out) or BILOU (Begin, Inside, Last, Out, Unit) encoding to represent the Named Entities. An example of one such command would be as follows:
			 </p><pre class="screen">
			   
$ opennlp TokenNameFinderTrainer -featuregen brown.xml -sequenceCodec BILOU -resources clusters/ \
-params PerceptronTrainerParams.txt -lang en -model ner-test.bin -data en-train.opennlp -encoding UTF-8
			 </pre><p>
		</p>
		</div>
		<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.training.api"></a>Training API</h3></div></div></div>
		
		<p>
			To train the name finder from within an application it is recommended to use the training
			API instead of the command line tool.
			Basically three steps are necessary to train it:
			</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>The application must open a sample data stream</p>
				</li><li class="listitem">
					<p>Call the NameFinderME.train method</p>
				</li><li class="listitem">
					<p>Save the TokenNameFinderModel to a file or database</p>
				</li></ul></div><p>
			The three steps are illustrated by the following sample code:
			</p><pre class="programlisting">
				
Charset charset = Charset.forName(<b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
ObjectStream&lt;String&gt; lineStream =
		<b class="hl-keyword">new</b> PlainTextByLineStream(<b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-ner-person.train"</i></b>), charset);
ObjectStream&lt;NameSample&gt; sampleStream = <b class="hl-keyword">new</b> NameSampleDataStream(lineStream);

TokenNameFinderModel model;

<b class="hl-keyword">try</b> {
  model = NameFinderME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, <b class="hl-string"><i style="color:red">"person"</i></b>, sampleStream, TrainingParameters.defaultParams(),
            TokenNameFinderFactory nameFinderFactory);
}
<b class="hl-keyword">finally</b> {
  sampleStream.close();
}

<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null) 
     modelOut.close();      
}
			 </pre><p>
		</p>
		</div>
		
		<div class="section" title="Custom Feature Generation"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.training.featuregen"></a>Custom Feature Generation</h3></div></div></div>
		
			<p>
				OpenNLP defines a default feature generation which is used when no custom feature
				generation is specified. Users which want to experiment with the feature generation
				can provide a custom feature generator. Either via API or via an xml descriptor file.
			</p>
			<div class="section" title="Feature Generation defined by API"><div class="titlepage"><div><div><h4 class="title"><a name="tools.namefind.training.featuregen.api"></a>Feature Generation defined by API</h4></div></div></div>
			
			<p>
				The custom generator must be used for training
				and for detecting the names. If the feature generation during training time and detection
				time is different the name finder might not be able to detect names.
				The following lines show how to construct a custom feature generator
				</p><pre class="programlisting">
					
AdaptiveFeatureGenerator featureGenerator = <b class="hl-keyword">new</b> CachedFeatureGenerator(
         <b class="hl-keyword">new</b> AdaptiveFeatureGenerator[]{
           <b class="hl-keyword">new</b> WindowFeatureGenerator(<b class="hl-keyword">new</b> TokenFeatureGenerator(), <span class="hl-number">2</span>, <span class="hl-number">2</span>),
           <b class="hl-keyword">new</b> WindowFeatureGenerator(<b class="hl-keyword">new</b> TokenClassFeatureGenerator(true), <span class="hl-number">2</span>, <span class="hl-number">2</span>),
           <b class="hl-keyword">new</b> OutcomePriorFeatureGenerator(),
           <b class="hl-keyword">new</b> PreviousMapFeatureGenerator(),
           <b class="hl-keyword">new</b> BigramNameFeatureGenerator(),
           <b class="hl-keyword">new</b> SentenceFeatureGenerator(true, false),
           <b class="hl-keyword">new</b> BrownTokenFeatureGenerator(BrownCluster dictResource)
           });
				</pre><p>
				which is similar to the default feature generator but with a BrownTokenFeature added.
				The javadoc of the feature generator classes explain what the individual feature generators do.
				To write a custom feature generator please implement the AdaptiveFeatureGenerator interface or
				if it must not be adaptive extend the FeatureGeneratorAdapter.
				The train method which should be used is defined as
				</p><pre class="programlisting">
					
<b class="hl-keyword">public</b> <b class="hl-keyword">static</b> TokenNameFinderModel train(String languageCode, String type,
          ObjectStream&lt;NameSample&gt; samples, TrainingParameters trainParams,
          TokenNameFinderFactory factory) <b class="hl-keyword">throws</b> IOException
				</pre><p>
				where the TokenNameFinderFactory allows to specify a custom feature generator.
				To detect names the model which was returned from the train method must be passed to the NameFinderME constructor.
				</p><pre class="programlisting">
					
<b class="hl-keyword">new</b> NameFinderME(model);
				 </pre><p>	 
			</p>
			</div>
			<div class="section" title="Feature Generation defined by XML Descriptor"><div class="titlepage"><div><div><h4 class="title"><a name="tools.namefind.training.featuregen.xml"></a>Feature Generation defined by XML Descriptor</h4></div></div></div>
			
			<p>
			OpenNLP can also use a xml descriptor file to configure the feature generation. The
            descriptor
			file is stored inside the model after training and the feature generators are configured
			correctly when the name finder is instantiated.
			
			The following sample shows a xml descriptor which contains the default feature generator plus several types of clustering features:
				</p><pre class="programlisting">
					
<b class="hl-tag" style="color: #000096">&lt;generators&gt;</b>
  <b class="hl-tag" style="color: #000096">&lt;cache&gt;</b> 
    <b class="hl-tag" style="color: #000096">&lt;generators&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;window</b> <span class="hl-attribute" style="color: #F5844C">prevLength</span> = <span class="hl-value" style="color: #993300">"2"</span> <span class="hl-attribute" style="color: #F5844C">nextLength</span> = <span class="hl-value" style="color: #993300">"2"</span><b class="hl-tag" style="color: #000096">&gt;</b>          
        <b class="hl-tag" style="color: #000096">&lt;tokenclass/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;/window&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;window</b> <span class="hl-attribute" style="color: #F5844C">prevLength</span> = <span class="hl-value" style="color: #993300">"2"</span> <span class="hl-attribute" style="color: #F5844C">nextLength</span> = <span class="hl-value" style="color: #993300">"2"</span><b class="hl-tag" style="color: #000096">&gt;</b>                
        <b class="hl-tag" style="color: #000096">&lt;token/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;/window&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;definition/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;prevmap/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;bigram/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;sentence</b> <span class="hl-attribute" style="color: #F5844C">begin</span>=<span class="hl-value" style="color: #993300">"true"</span> <span class="hl-attribute" style="color: #F5844C">end</span>=<span class="hl-value" style="color: #993300">"false"</span><b class="hl-tag" style="color: #000096">/&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;window</b> <span class="hl-attribute" style="color: #F5844C">prevLength</span> = <span class="hl-value" style="color: #993300">"2"</span> <span class="hl-attribute" style="color: #F5844C">nextLength</span> = <span class="hl-value" style="color: #993300">"2"</span><b class="hl-tag" style="color: #000096">&gt;</b>
        <b class="hl-tag" style="color: #000096">&lt;brownclustertoken</b> <span class="hl-attribute" style="color: #F5844C">dict</span>=<span class="hl-value" style="color: #993300">"brownCluster"</span><b class="hl-tag" style="color: #000096"> /&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;/window&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;brownclustertokenclass</b> <span class="hl-attribute" style="color: #F5844C">dict</span>=<span class="hl-value" style="color: #993300">"brownCluster"</span><b class="hl-tag" style="color: #000096"> /&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;brownclusterbigram</b> <span class="hl-attribute" style="color: #F5844C">dict</span>=<span class="hl-value" style="color: #993300">"brownCluster"</span><b class="hl-tag" style="color: #000096"> /&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;wordcluster</b> <span class="hl-attribute" style="color: #F5844C">dict</span>=<span class="hl-value" style="color: #993300">"word2vec.cluster"</span><b class="hl-tag" style="color: #000096"> /&gt;</b>
      <b class="hl-tag" style="color: #000096">&lt;wordcluster</b> <span class="hl-attribute" style="color: #F5844C">dict</span>=<span class="hl-value" style="color: #993300">"clark.cluster"</span><b class="hl-tag" style="color: #000096"> /&gt;</b>
    <b class="hl-tag" style="color: #000096">&lt;/generators&gt;</b>
  <b class="hl-tag" style="color: #000096">&lt;/cache&gt;</b> 
<b class="hl-tag" style="color: #000096">&lt;/generators&gt;</b>
				 </pre><p>
		    The root element must be generators, each sub-element adds a feature generator to the configuration.
		    The sample xml is constains aditional feature generators with respect to the API defined above.
			</p>
			<p>
			The following table shows the supported elements:
			</p><div class="table"><a name="d4e278"></a><p class="title"><b>Table&nbsp;4.1.&nbsp;Generator elements</b></p><div class="table-contents">
			  
			  <table summary="Generator elements" border="1"><colgroup><col><col></colgroup><thead><tr><th>Element</th><th>Aggregated</th><th>Attributes</th></tr></thead><tbody><tr><td>generators</td><td>yes</td><td>none</td></tr><tr><td>cache</td><td>yes</td><td>none</td></tr><tr><td>charngram</td><td>no</td><td><span class="emphasis"><em>min</em></span> and <span class="emphasis"><em>max</em></span> specify the length of the generated character ngrams</td></tr><tr><td>definition</td><td>no</td><td>none</td></tr><tr><td>dictionary</td><td>no</td><td><span class="emphasis"><em>dict</em></span> is the key of the dictionary resource to use,
					       and <span class="emphasis"><em>prefix</em></span> is a feature prefix string</td></tr><tr><td>prevmap</td><td>no</td><td>none</td></tr><tr><td>sentence</td><td>no</td><td><span class="emphasis"><em>begin</em></span> and <span class="emphasis"><em>end</em></span> to generate begin or end features, both are optional and are boolean values</td></tr><tr><td>tokenclass</td><td>no</td><td>none</td></tr><tr><td>token</td><td>no</td><td>none</td></tr><tr><td>bigram</td><td>no</td><td>none</td></tr><tr><td>tokenpattern</td><td>no</td><td>none</td></tr><tr><td>wordcluster</td><td>no</td><td><span class="emphasis"><em>dict</em></span> is the key of the clustering resource to use</td></tr><tr><td>brownclustertoken</td><td>no</td><td><span class="emphasis"><em>dict</em></span> is the key of the clustering resource to use</td></tr><tr><td>brownclustertokenclass</td><td>no</td><td><span class="emphasis"><em>dict</em></span> is the key of the clustering resource to use</td></tr><tr><td>brownclusterbigram</td><td>no</td><td><span class="emphasis"><em>dict</em></span> is the key of the clustering resource to use</td></tr><tr><td>window</td><td>yes</td><td><span class="emphasis"><em>prevLength</em></span> and <span class="emphasis"><em>nextLength</em></span> must be integers ans specify the window size</td></tr><tr><td>custom</td><td>no</td><td><span class="emphasis"><em>class</em></span> is the name of the feature generator class which will be loaded</td></tr></tbody></table>
			</div></div><p><br class="table-break">
			Aggregated feature generators can contain other generators, like the cache or the window feature
			generator in the sample.
			</p>
			</div>
		</div>
	</div>
	<div class="section" title="Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.namefind.eval"></a>Evaluation</h2></div></div></div>
		
		<p>
		The built in evaluation can measure the named entity recognition performance of the name finder.
		The performance is either measured on a test dataset or via cross validation.
		</p>
		<div class="section" title="Evaluation Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.eval.tool"></a>Evaluation Tool</h3></div></div></div>
		
		<p>
		The following command shows how the tool can be run:
		</p><pre class="screen">
            
$ opennlp TokenNameFinderEvaluator -model en-ner-person.bin -data en-ner-person.test -encoding UTF-8

Precision: 0.8005071889818507
Recall: 0.7450581122145297
F-Measure: 0.7717879983140168
         </pre><p>
			 Note: The command line interface does not support cross evaluation in the current version.
		</p>
		</div>
		<div class="section" title="Evaluation API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.namefind.eval.api"></a>Evaluation API</h3></div></div></div>
		
		<p>
		The evaluation can be performed on a pre-trained model and a test dataset or via cross validation.
		In the first case the model must be loaded and a NameSample ObjectStream must be created (see code samples above),
		assuming these two objects exist the following code shows how to perform the evaluation:
			</p><pre class="programlisting">
				
TokenNameFinderEvaluator evaluator = <b class="hl-keyword">new</b> TokenNameFinderEvaluator(<b class="hl-keyword">new</b> NameFinderME(model));
evaluator.evaluate(sampleStream);

FMeasure result = evaluator.getFMeasure();

System.out.println(result.toString());
			</pre><p>
			In the cross validation case all the training arguments must be
			provided (see the Training API section above).
			To perform cross validation the ObjectStream must be resettable.
			</p><pre class="programlisting">
				
FileInputStream sampleDataIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-ner-person.train"</i></b>);
ObjectStream&lt;NameSample&gt; sampleStream = <b class="hl-keyword">new</b> PlainTextByLineStream(sampleDataIn.getChannel(), <b class="hl-string"><i style="color:red">"UTF-8"</i></b>); 
TokenNameFinderCrossValidator evaluator = <b class="hl-keyword">new</b> TokenNameFinderCrossValidator(<b class="hl-string"><i style="color:red">"en"</i></b>, <span class="hl-number">100</span>, <span class="hl-number">5</span>);
evaluator.evaluate(sampleStream, <span class="hl-number">10</span>);

FMeasure result = evaluator.getFMeasure();

System.out.println(result.toString());
			</pre><p>
		</p>
		</div>
	</div>
			<div class="section" title="Named Entity Annotation Guidelines"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.namefind.annotation_guides"></a>Named Entity Annotation Guidelines</h2></div></div></div>
		
		<p>
			Annotation guidelines define what should be labeled as an entity. To build
			a private corpus it is important to know these guidelines and maybe write a
			custom one.
			Here is a list of publicly available annotation guidelines:
				</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
					<p>
						<a class="ulink" href="http://cs.nyu.edu/cs/faculty/grishman/NEtask20.book_1.html" target="_top">
							MUC6
						</a>
					</p>
				</li><li class="listitem">
					<p>
						<a class="ulink" href="http://acl.ldc.upenn.edu/muc7/ne_task.html" target="_top">
							MUC7
						</a>
					</p>
				</li><li class="listitem">
					<p>
						<a class="ulink" href="http://projects.ldc.upenn.edu/ace/docs/English-Entities-Guidelines_v5.6.1.pdf" target="_top">
							ACE
						</a>
					</p>
				</li><li class="listitem">
                    <p>
                        <a class="ulink" href="http://www.cnts.ua.ac.be/conll2002/ner/" target="_top">
                            CONLL 2002
                        </a>
                    </p>
                </li><li class="listitem">
					<p>
						<a class="ulink" href="http://www.cnts.ua.ac.be/conll2003/ner/annotation.txt" target="_top">
							CONLL 2003
						</a>
					</p>
				</li></ul></div><p>
		</p>
		</div>
</div>
	<div class="chapter" title="Chapter&nbsp;5.&nbsp;Document Categorizer"><div class="titlepage"><div><div><h2 class="title"><a name="tools.doccat"></a>Chapter&nbsp;5.&nbsp;Document Categorizer</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.doccat.classifying">Classifying</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.doccat.classifying.cmdline">Document Categorizer Tool</a></span></dt><dt><span class="section"><a href="#tools.doccat.classifying.api">Document Categorizer API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.doccat.training">Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.doccat.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.doccat.training.api">Training API</a></span></dt></dl></dd></dl></div>

	<div class="section" title="Classifying"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.doccat.classifying"></a>Classifying</h2></div></div></div>
		
		<p>
		The OpenNLP Document Categorizer can classify text into pre-defined categories. 
		It is based on maximum entropy framework. For someone interested in Gross Margin,
		the sample text given below could be classified as GMDecrease
        </p><pre class="screen">
			
Major acquisitions that have a lower gross margin than the existing network
also had a negative impact on the overall gross margin, but it should improve
following the implementation of its integration strategies.
		 </pre><p>
and the text below could be classified as GMIncrease
        </p><pre class="screen">
			
The upward movement of gross margin resulted from amounts pursuant to 
adjustments to obligations towards dealers.
		 </pre><p>
		 To be able to classify a text, the document categorizer needs a model. 
		 The classifications are requirements-specific
		 and hence there is no pre-built model for document categorizer under OpenNLP project.
		</p>		
	
	<div class="section" title="Document Categorizer Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.doccat.classifying.cmdline"></a>Document Categorizer Tool</h3></div></div></div>
		
		<p>
		The easiest way to try out the document categorizer is the command line tool. The tool is only
		intended for demonstration and testing. The following command shows how to use the document categorizer tool. 
		  </p><pre class="screen">
			
$ opennlp Doccat model
		 </pre><p>
		 The input is read from standard input and output is written to standard output, unless they are redirected
		 or piped. As with most components in OpenNLP, document categorizer expects input which is segmented into sentences.
		</p>
 	 </div>
  	<div class="section" title="Document Categorizer API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.doccat.classifying.api"></a>Document Categorizer API</h3></div></div></div>
		
		<p>
			To perform classification you will need a maxent model -
			these are encapsulated in the DoccatModel class of OpenNLP tools.
		</p>
		<p>
			First you need to grab the bytes from the serialized model on an InputStream - 
			we'll leave it you to do that, since you were the one who serialized it to begin with. Now for the easy part:
						</p><pre class="programlisting">
				
InputStream is = ...
DoccatModel m = <b class="hl-keyword">new</b> DoccatModel(is);
				</pre><p>
				With the DoccatModel in hand we are just about there:
						</p><pre class="programlisting">
				
String inputText = ...
DocumentCategorizerME myCategorizer = <b class="hl-keyword">new</b> DocumentCategorizerME(m);
<b class="hl-keyword">double</b>[] outcomes = myCategorizer.categorize(inputText);
String category = myCategorizer.getBestCategory(outcomes);
				</pre><p>
		</p>
	</div>
	</div>
	<div class="section" title="Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.doccat.training"></a>Training</h2></div></div></div>
	
		<p>
			The Document Categorizer can be trained on annotated training material. The data
			can be in OpenNLP Document Categorizer training format. This is one document per line,
			containing category and text separated by a whitespace. Other formats can also be
            available.
			The following sample shows the sample from above in the required format. Here GMDecrease and GMIncrease
			are the categories.
			</p><pre class="screen">
			
GMDecrease Major acquisitions that have a lower gross margin than the existing network also \ 
           had a negative impact on the overall gross margin, but it should improve following \ 
           the implementation of its integration strategies .
GMIncrease The upward movement of gross margin resulted from amounts pursuant to adjustments \
           to obligations towards dealers .
			</pre><p>
			Note: The line breaks marked with a backslash are just inserted for formatting purposes and must not be
			included in the training data.
		</p>
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.doccat.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
		The following command will train the document categorizer and write the model to en-doccat.bin:		
		  </p><pre class="screen">
						
$ opennlp DoccatTrainer -model en-doccat.bin -lang en -data en-doccat.train -encoding UTF-8
		 </pre><p>
		Additionally it is possible to specify the number of iterations, and the cutoff.
		</p>
		</div>
				<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.doccat.training.api"></a>Training API</h3></div></div></div>
		
		<p>
		So, naturally you will need some access to many pre-classified events to train your model.
		The class opennlp.tools.doccat.DocumentSample encapsulates a text document and its classification.
		DocumentSample has two constructors. Each take the text's category as one argument. The other argument can either be raw
		text, or an array of tokens. By default, the raw text will be split into tokens by whitespace. So, let's say
		your training data was contained in a text file, where the format is as described above.
		Then you might want to write something like this to create a collection of DocumentSamples:
		</p><pre class="programlisting">
						
DoccatModel model = null;

InputStream dataIn = null;
<b class="hl-keyword">try</b> {
  dataIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-sentiment.train"</i></b>);
  ObjectStream&lt;String&gt; lineStream =
		<b class="hl-keyword">new</b> PlainTextByLineStream(dataIn, <b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
  ObjectStream&lt;DocumentSample&gt; sampleStream = <b class="hl-keyword">new</b> DocumentSampleStream(lineStream);

  model = DocumentCategorizerME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, sampleStream);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Failed to read or parse training data, training failed</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (dataIn != null) {
    <b class="hl-keyword">try</b> {
      dataIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
      <i class="hl-comment" style="color: silver">// Not an issue, training already finished.</i>
      <i class="hl-comment" style="color: silver">// The exception should be logged and investigated</i>
      <i class="hl-comment" style="color: silver">// if part of a production system.</i>
      e.printStackTrace();
    }
  }
}
	</pre><p>
	Now might be a good time to cruise over to Hulu or something, because this could take a while if you've got a large training set.
	You may see a lot of output as well. Once you're done, you can pretty quickly step to classification directly,
	but first we'll cover serialization. Feel free to skim.
		</p>
		<p>
		</p><pre class="programlisting">
						
OutputStream modelOut = null;
<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Failed to save model</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null) {
    <b class="hl-keyword">try</b> {
       modelOut.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
      <i class="hl-comment" style="color: silver">// Failed to correctly save model.</i>
      <i class="hl-comment" style="color: silver">// Written model might be invalid.</i>
      e.printStackTrace();
    }
  }
}
</pre><p>
		</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;6.&nbsp;Part-of-Speech Tagger"><div class="titlepage"><div><div><h2 class="title"><a name="tools.postagger"></a>Chapter&nbsp;6.&nbsp;Part-of-Speech Tagger</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.postagger.tagging">Tagging</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.tagging.cmdline">POS Tagger Tool</a></span></dt><dt><span class="section"><a href="#tools.postagger.tagging.api">POS Tagger API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.postagger.training">Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.postagger.training.api">Training API</a></span></dt><dt><span class="section"><a href="#tools.postagger.training.tagdict">Tag Dictionary</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.postagger.eval">Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.postagger.eval.tool">Evaluation Tool</a></span></dt></dl></dd></dl></div>

	<div class="section" title="Tagging"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.postagger.tagging"></a>Tagging</h2></div></div></div>
		
		<p>
		The Part of Speech Tagger marks tokens with their corresponding word type
		based on the token itself and the context of the token. A token might have
		multiple pos tags depending on the token and the context. The OpenNLP POS Tagger
		uses a probability model to predict the correct pos tag out of the tag set.
		To limit the possible tags for a token a tag dictionary can be used which increases
		the tagging and runtime performance of the tagger.
		</p>
			<div class="section" title="POS Tagger Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.tagging.cmdline"></a>POS Tagger Tool</h3></div></div></div>
		
		<p>
		The easiest way to try out the POS Tagger is the command line tool. The tool is
		only intended for demonstration and testing.
		Download the english maxent pos model and start the POS Tagger Tool with this command:
		</p><pre class="screen">
			
$ opennlp POSTagger en-pos-maxent.bin
		 </pre><p>
		The POS Tagger now reads a tokenized sentence per line from stdin.
		Copy these two sentences to the console:
		</p><pre class="screen">
			
Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
Mr. Vinken is chairman of Elsevier N.V. , the Dutch publishing group .
		 </pre><p>
		 the POS Tagger will now echo the sentences with pos tags to the console:
		</p><pre class="screen">
			
Pierre_NNP Vinken_NNP ,_, 61_CD years_NNS old_JJ ,_, will_MD join_VB the_DT board_NN as_IN
    a_DT nonexecutive_JJ director_NN Nov._NNP 29_CD ._.
Mr._NNP Vinken_NNP is_VBZ chairman_NN of_IN Elsevier_NNP N.V._NNP ,_, the_DT Dutch_NNP publishing_VBG group_NN
		 </pre><p>
		 The tag set used by the english pos model is the <a class="ulink" href="http://www.cis.upenn.edu/~treebank/" target="_top">Penn Treebank tag set</a>.
		</p>
      </div>
      
		<div class="section" title="POS Tagger API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.tagging.api"></a>POS Tagger API</h3></div></div></div>
		
		<p>
		    The POS Tagger can be embedded into an application via its API.
			First the pos model must be loaded into memory from disk or an other source.
			In the sample below its loaded from disk.
			</p><pre class="programlisting">
				
InputStream modelIn = null;

<b class="hl-keyword">try</b> {
  modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-pos-maxent.bin"</i></b>);
  POSModel model = <b class="hl-keyword">new</b> POSModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Model loading failed, handle the error</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
			</pre><p>
			After the model is loaded the POSTaggerME can be instantiated.
			</p><pre class="programlisting">
				
POSTaggerME tagger = <b class="hl-keyword">new</b> POSTaggerME(model);
			</pre><p>
			The POS Tagger instance is now ready to tag data. It expects a tokenized sentence
			as input, which is represented as a String array, each String object in the array
			is one token.
	   </p>
	   <p>
	   The following code shows how to determine the most likely pos tag sequence for a sentence.
	   	</p><pre class="programlisting">
		  
String sent[] = <b class="hl-keyword">new</b> String[]{<b class="hl-string"><i style="color:red">"Most"</i></b>, <b class="hl-string"><i style="color:red">"large"</i></b>, <b class="hl-string"><i style="color:red">"cities"</i></b>, <b class="hl-string"><i style="color:red">"in"</i></b>, <b class="hl-string"><i style="color:red">"the"</i></b>, <b class="hl-string"><i style="color:red">"US"</i></b>, <b class="hl-string"><i style="color:red">"had"</i></b>,
                             <b class="hl-string"><i style="color:red">"morning"</i></b>, <b class="hl-string"><i style="color:red">"and"</i></b>, <b class="hl-string"><i style="color:red">"afternoon"</i></b>, <b class="hl-string"><i style="color:red">"newspapers"</i></b>, <b class="hl-string"><i style="color:red">"."</i></b>};		  
String tags[] = tagger.tag(sent);
			</pre><p>
			The tags array contains one part-of-speech tag for each token in the input array. The corresponding
			tag can be found at the same index as the token has in the input array.
			The confidence scores for the returned tags can be easily retrieved from
			a POSTaggerME with the following method call:
				   	</p><pre class="programlisting">
		  
<b class="hl-keyword">double</b> probs[] = tagger.probs();
			</pre><p>
			The call to probs is stateful and will always return the probabilities of the last
			tagged sentence. The probs method should only be called when the tag method
			was called before, otherwise the behavior is undefined.
			</p>
			<p>
			Some applications need to retrieve the n-best pos tag sequences and not
			only the best sequence.
			The topKSequences method is capable of returning the top sequences.
			It can be called in a similar way as tag.
			</p><pre class="programlisting">
		  
Sequence topSequences[] = tagger.topKSequences(sent);
			</pre><p>	
			Each Sequence object contains one sequence. The sequence can be retrieved
			via Sequence.getOutcomes() which returns a tags array 
			and Sequence.getProbs() returns the probability array for this sequence.
	  		 </p>
	</div>
	</div>
		<div class="section" title="Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.postagger.training"></a>Training</h2></div></div></div>
		
		<p>
			The POS Tagger can be trained on annotated training material. The training material
			is a collection of tokenized sentences where each token has the assigned part-of-speech tag.
			The native POS Tagger training material looks like this:
			</p><pre class="screen">
		  
About_IN 10_CD Euro_NNP ,_, I_PRP reckon_VBP ._.
That_DT sounds_VBZ good_JJ ._.
			</pre><p>
			Each sentence must be in one line. The token/tag pairs are combined with "_".
			The token/tag pairs are whitespace separated. The data format does not
			define a document boundary. If a document boundary should be included in the
			training material it is suggested to use an empty line.
		</p>
		<p>The Part-of-Speech Tagger can either be trained with a command line tool,
		or via an training API.
		</p>
		
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
			OpenNLP has a command line tool which is used to train the models available from the model
			download page on various corpora.
		</p>
		<p>
		    Usage of the tool:
            </p><pre class="screen">
				
$ opennlp POSTaggerTrainer
Usage: opennlp POSTaggerTrainer[.conllx] [-type maxent|perceptron|perceptron_sequence] \
               [-dict dictionaryPath] [-ngram cutoff] [-params paramsFile] [-iterations num] \
               [-cutoff num] -model modelFile -lang language -data sampleData \
               [-encoding charsetName]

Arguments description:
        -type maxent|perceptron|perceptron_sequence
                The type of the token name finder model. One of maxent|perceptron|perceptron_sequence.
        -dict dictionaryPath
                The XML tag dictionary file
        -ngram cutoff
                NGram cutoff. If not specified will not create ngram dictionary.
        -params paramsFile
                training parameters file.
        -iterations num
                number of training iterations, ignored if -params is used.
        -cutoff num
                minimal number of times a feature must be seen, ignored if -params is used.
        -model modelFile
                output model file.
        -lang language
                language which is being processed.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
			 </pre><p>
		</p>
		<p>
		    The following command illustrates how an english part-of-speech model can be trained:
		    </p><pre class="screen">
		  
$ opennlp POSTaggerTrainer -type maxent -model en-pos-maxent.bin \
                           -lang en -data en-pos.train -encoding UTF-8
		    </pre><p>
		</p>
		</div>
		<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.training.api"></a>Training API</h3></div></div></div>
		
		<p>
		The Part-of-Speech Tagger training API supports the training of a new pos model.
		Basically three steps are necessary to train it:
		</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
				<p>The application must open a sample data stream</p>
			</li><li class="listitem">
				<p>Call the POSTagger.train method</p>
			</li><li class="listitem">
				<p>Save the POSModel to a file or database</p>
			</li></ul></div><p>
		The following code illustrates that:
		</p><pre class="programlisting">
				
POSModel model = null;

InputStream dataIn = null;
<b class="hl-keyword">try</b> {
  dataIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-pos.train"</i></b>);
  ObjectStream&lt;String&gt; lineStream = <b class="hl-keyword">new</b> PlainTextByLineStream(dataIn, <b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
  ObjectStream&lt;POSSample&gt; sampleStream = <b class="hl-keyword">new</b> WordTagSampleStream(lineStream);

  model = POSTaggerME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, sampleStream, TrainingParameters.defaultParams(), null, null);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Failed to read or parse training data, training failed</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (dataIn != null) {
    <b class="hl-keyword">try</b> {
      dataIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
      <i class="hl-comment" style="color: silver">// Not an issue, training already finished.</i>
      <i class="hl-comment" style="color: silver">// The exception should be logged and investigated</i>
      <i class="hl-comment" style="color: silver">// if part of a production system.</i>
      e.printStackTrace();
    }
  }
}
	</pre><p>
	The above code performs the first two steps, opening the data and training
	the model. The trained model must still be saved into an OutputStream, in
	the sample below it is written into a file.
	</p><pre class="programlisting">
				
OutputStream modelOut = null;
<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Failed to save model</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null) {
  <b class="hl-keyword">try</b> {
     modelOut.close();
  }
  <b class="hl-keyword">catch</b> (IOException e) {
    <i class="hl-comment" style="color: silver">// Failed to correctly save model.</i>
    <i class="hl-comment" style="color: silver">// Written model might be invalid.</i>
    e.printStackTrace();
  }
}
		</pre><p>
		</p>
		</div>
		<div class="section" title="Tag Dictionary"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.training.tagdict"></a>Tag Dictionary</h3></div></div></div>
		
		<p>
		The tag dictionary is a word dictionary which specifies which tags a specific token can have. Using a tag
		dictionary has two advantages, inappropriate tags can not been assigned to tokens in the dictionary and the
		beam search algorithm has to consider less possibilities and can search faster.
		</p>
		<p>
		The dictionary is defined in a xml format and can be created and stored with the POSDictionary class.
		Please for now checkout the javadoc and source code of that class.
		</p>
		<p>Note: The format should be documented and sample code should show how to use the dictionary.
			  Any contributions are very welcome. If you want to contribute please contact us on the mailing list
			  or comment on the jira issue <a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-287" target="_top">OPENNLP-287</a>.
		</p>
		</div>
		</div>
		
		<div class="section" title="Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.postagger.eval"></a>Evaluation</h2></div></div></div>
		
		<p>
		The built in evaluation can measure the accuracy of the pos tagger.
		The accuracy can be measured on a test data set or via cross validation.
		</p>
		<div class="section" title="Evaluation Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.postagger.eval.tool"></a>Evaluation Tool</h3></div></div></div>
		
		<p>
		There is a command line tool to evaluate a given model on a test data set.
		The following command shows how the tool can be run:
		</p><pre class="screen">
				
$ opennlp POSTaggerEvaluator -model pt.postagger.bin -data pt.postagger.test -encoding utf-8
			 </pre><p>
			 This will display the resulting accuracy score, e.g.:
			 </p><pre class="screen">
				
Loading model ... done
Evaluating ... done

Accuracy: 0.9659110277825124
			 </pre><p>
		</p>
            <p>
            There is a command line tool to cross validate a test data set.
            The following command shows how the tool can be run:
            </p><pre class="screen">
                    
$ opennlp POSTaggerCrossValidator -lang pt -data pt.postagger.test -encoding utf-8
                 </pre><p>
                 This will display the resulting accuracy score, e.g.:
                 </p><pre class="screen">
                    
Accuracy: 0.9659110277825124
                 </pre><p>
            </p>

		</div>
		</div>
</div>
	<div class="chapter" title="Chapter&nbsp;7.&nbsp;Lemmatizer"><div class="titlepage"><div><div><h2 class="title"><a name="tools.lemmatizer"></a>Chapter&nbsp;7.&nbsp;Lemmatizer</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.lemmatizer.tagging.cmdline">Lemmatizer Tool</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.tagging.api">Lemmatizer API</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.training">Lemmatizer Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.lemmatizer.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.lemmatizer.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.lemmatizer.evaluation">Lemmatizer Evaluation</a></span></dt></dl></div>
	
		<p>
			The lemmatizer returns, for a given word form (token) and Part of Speech
			tag,
			the dictionary form of a word, which is usually referred to as its
			lemma. A token could
			ambiguously be derived from several basic forms or dictionary words which is why
			the
			postag of the word is required to find the lemma. For example, the form
			`show' may refer
			to either the verb "to show" or to the noun "show".
			Currently OpenNLP implement statistical and dictionary-based lemmatizers.
		</p>
		<div class="section" title="Lemmatizer Tool"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.lemmatizer.tagging.cmdline"></a>Lemmatizer Tool</h2></div></div></div>
			
			<p>
				The easiest way to try out the Lemmatizer is the command line tool,
				which provides access to the statistical
				lemmatizer. Note that the tool is only intended for demonstration and testing.
			</p>
			<p>
				Once you have trained a lemmatizer model (see below for instructions),
				you can start the Lemmatizer Tool with this command:
			</p>
			<p>
				</p><pre class="screen">
		   
$ opennlp LemmatizerME en-lemmatizer.bin &lt; sentences
		  </pre><p>
				The Lemmatizer now reads a pos tagged sentence(s) per line from
				standard input. For example, you can copy this sentence to the
				console:
				</p><pre class="screen">
		    
Rockwell_NNP International_NNP Corp._NNP 's_POS Tulsa_NNP unit_NN said_VBD it_PRP 
signed_VBD a_DT tentative_JJ agreement_NN extending_VBG its_PRP$ contract_NN with_IN
Boeing_NNP Co._NNP to_TO provide_VB structural_JJ parts_NNS for_IN Boeing_NNP 's_POS 
747_CD jetliners_NNS ._.
		  </pre><p>
				The Lemmatizer will now echo the lemmas for each word postag pair to
				the console:
				</p><pre class="screen">
		    
Rockwell NNP rockwell
International NNP international
Corp. NNP corp.
's POS 's
Tulsa NNP tulsa
unit NN unit
said VBD say
it PRP it
signed VBD sign
...

		  </pre><p>
			</p>
		</div>
		<div class="section" title="Lemmatizer API"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.lemmatizer.tagging.api"></a>Lemmatizer API</h2></div></div></div>
			
			<p>
				The Lemmatizer can be embedded into an application via its API.
				Currently a statistical
				and DictionaryLemmatizer are available. Note that these two methods are
				complementary and
				the DictionaryLemmatizer can also be used as a way of post-processing
				the output of the statistical
				lemmatizer.
			</p>
			<p>
				The statistical lemmatizer requires that a trained model is loaded
				into memory from disk or from another source.
				In the example below it is loaded from disk:
				</p><pre class="programlisting">
		
InputStream modelIn = null;
LemmatizerModel model = null;
<b class="hl-keyword">try</b> {
  modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-lemmatizer.bin"</i></b>);
  model = <b class="hl-keyword">new</b> LemmatizerModel(modelIn);
} <b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Model loading failed, handle the error</i>
  e.printStackTrace();
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
    modelIn.close();
    } <b class="hl-keyword">catch</b> (IOException e) {
      }
  }
}
			</pre><p>
				After the model is loaded a LemmatizerME can be instantiated.
				</p><pre class="programlisting">
				
LemmatizerME lemmatizer = <b class="hl-keyword">new</b> LemmatizerME(model);
			</pre><p>
				The Lemmatizer instance is now ready to lemmatize data. It expects a
				tokenized sentence
				as input, which is represented as a String array, each String object
				in the array
				is one token, and the POS tags associated with each token.
			</p>
			<p>
				The following code shows how to determine the most likely lemma for
				a sentence.
				</p><pre class="programlisting">
		  
String[] tokens = <b class="hl-keyword">new</b> String[] { <b class="hl-string"><i style="color:red">"Rockwell"</i></b>, <b class="hl-string"><i style="color:red">"International"</i></b>, <b class="hl-string"><i style="color:red">"Corp."</i></b>, <b class="hl-string"><i style="color:red">"'s"</i></b>,
    <b class="hl-string"><i style="color:red">"Tulsa"</i></b>, <b class="hl-string"><i style="color:red">"unit"</i></b>, <b class="hl-string"><i style="color:red">"said"</i></b>, <b class="hl-string"><i style="color:red">"it"</i></b>, <b class="hl-string"><i style="color:red">"signed"</i></b>, <b class="hl-string"><i style="color:red">"a"</i></b>, <b class="hl-string"><i style="color:red">"tentative"</i></b>, <b class="hl-string"><i style="color:red">"agreement"</i></b>,
    <b class="hl-string"><i style="color:red">"extending"</i></b>, <b class="hl-string"><i style="color:red">"its"</i></b>, <b class="hl-string"><i style="color:red">"contract"</i></b>, <b class="hl-string"><i style="color:red">"with"</i></b>, <b class="hl-string"><i style="color:red">"Boeing"</i></b>, <b class="hl-string"><i style="color:red">"Co."</i></b>, <b class="hl-string"><i style="color:red">"to"</i></b>,
    <b class="hl-string"><i style="color:red">"provide"</i></b>, <b class="hl-string"><i style="color:red">"structural"</i></b>, <b class="hl-string"><i style="color:red">"parts"</i></b>, <b class="hl-string"><i style="color:red">"for"</i></b>, <b class="hl-string"><i style="color:red">"Boeing"</i></b>, <b class="hl-string"><i style="color:red">"'s"</i></b>, <b class="hl-string"><i style="color:red">"747"</i></b>,
    <b class="hl-string"><i style="color:red">"jetliners"</i></b>, <b class="hl-string"><i style="color:red">"."</i></b> };

String[] postags = <b class="hl-keyword">new</b> String[] { <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"POS"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>,
    <b class="hl-string"><i style="color:red">"VBD"</i></b>, <b class="hl-string"><i style="color:red">"PRP"</i></b>, <b class="hl-string"><i style="color:red">"VBD"</i></b>, <b class="hl-string"><i style="color:red">"DT"</i></b>, <b class="hl-string"><i style="color:red">"JJ"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>, <b class="hl-string"><i style="color:red">"VBG"</i></b>, <b class="hl-string"><i style="color:red">"PRP$"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>, <b class="hl-string"><i style="color:red">"IN"</i></b>,
    <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"TO"</i></b>, <b class="hl-string"><i style="color:red">"VB"</i></b>, <b class="hl-string"><i style="color:red">"JJ"</i></b>, <b class="hl-string"><i style="color:red">"NNS"</i></b>, <b class="hl-string"><i style="color:red">"IN"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"POS"</i></b>, <b class="hl-string"><i style="color:red">"CD"</i></b>, <b class="hl-string"><i style="color:red">"NNS"</i></b>,
    <b class="hl-string"><i style="color:red">"."</i></b> };

String[] lemmas = lemmatizer.lemmatize(tokens, postags);
String[] decodedLemmas = lemmatizer.decodeLemmas(tokens, lemmas);
		</pre><p>
				The decodedLemmas array contains one lemma for each token in the
				input array. The corresponding
				tag and lemma can be found at the same index as the token has in the
				input array.
			</p>

			<p>
				The DictionaryLemmatizer is constructed
				by passing the InputStream of a lemmatizer dictionary. Such dictionary
				consists of a
				text file containing, for each row, a word, its postag and the
				corresponding lemma:
				</p><pre class="screen">
		
show    NN      show
showcase        NN      showcase
showcases       NNS     showcase
showdown        NN      showdown
showdowns       NNS     showdown
shower  NN      shower
showers NNS     shower
showman NN      showman
showmanship     NN      showmanship
showmen NNS     showman
showroom        NN      showroom
showrooms       NNS     showroom
shows   NNS     show
showstopper     NN      showstopper
showstoppers    NNS     showstopper
shrapnel        NN      shrapnel
		
		</pre><p>
				First the dictionary must be loaded into memory from disk or another
				source.
				In the sample below it is loaded from disk.
				</p><pre class="programlisting">
				
InputStream dictLemmatizer = null;

<b class="hl-keyword">try</b> {
  dictLemmatizer = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"english-lemmatizer.txt"</i></b>);
}
<b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// dictionary loading failed, handle the error</i>
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (dictLemmatizer != null) {
    <b class="hl-keyword">try</b> {
      dictLemmatizer.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
			</pre><p>
				After the dictionary is loaded the DictionaryLemmatizer can be
				instantiated.
				</p><pre class="programlisting">
			  
DictionaryLemmatizer lemmatizer = <b class="hl-keyword">new</b> DictionaryLemmatizer(dictLemmatizer);
			</pre><p>
				The DictionaryLemmatizer instance is now ready. It expects two
				String arrays as input,
				a containing the tokens and another one their respective postags.
			</p>
			<p>
				The following code shows how to find a lemma using a
				DictionaryLemmatizer. There is no need to decode the
				lemmas when using the DictionaryLemmatizer.
				</p><pre class="programlisting">
		  
String[] tokens = <b class="hl-keyword">new</b> String[]{<b class="hl-string"><i style="color:red">"Most"</i></b>, <b class="hl-string"><i style="color:red">"large"</i></b>, <b class="hl-string"><i style="color:red">"cities"</i></b>, <b class="hl-string"><i style="color:red">"in"</i></b>, <b class="hl-string"><i style="color:red">"the"</i></b>, <b class="hl-string"><i style="color:red">"US"</i></b>, <b class="hl-string"><i style="color:red">"had"</i></b>,
                             <b class="hl-string"><i style="color:red">"morning"</i></b>, <b class="hl-string"><i style="color:red">"and"</i></b>, <b class="hl-string"><i style="color:red">"afternoon"</i></b>, <b class="hl-string"><i style="color:red">"newspapers"</i></b>, <b class="hl-string"><i style="color:red">"."</i></b>};
String[] tags = tagger.tag(sent);
String[] lemmas = lemmatizer.lemmatize(tokens, postags);

			</pre><p>
				The tags array contains one part-of-speech tag for each token in the
				input array. The corresponding
				tag and lemmas can be found at the same index as the token has in the
				input array.
			</p>
		</div>
		<div class="section" title="Lemmatizer Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.lemmatizer.training"></a>Lemmatizer Training</h2></div></div></div>
			
			<p>
				The training data consist of three columns separated by spaces. Each
				word has been put on a
				separate line and there is an empty line after each sentence. The first
				column contains
				the current word, the second its part-of-speech tag and the third its
				lemma.
				Here is an example of the file format:
			</p>
			<p>
				Sample sentence of the training data:
				</p><pre class="screen">
		
He        PRP  he
reckons   VBZ  reckon
the       DT   the
current   JJ   current
accounts  NNS  account
deficit   NN   deficit
will      MD   will
narrow    VB   narrow
to        TO   to
only      RB   only
#         #    #
1.8       CD   1.8
millions  CD   million
in        IN   in
September NNP  september
.         .    O
		</pre><p>
				The Universal Dependencies Treebank and the CoNLL 2009 datasets
				distribute training data for many languages.
			</p>
			<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.lemmatizer.training.tool"></a>Training Tool</h3></div></div></div>
				
				<p>
					OpenNLP has a command line tool which is used to train the models on
					various corpora.
				</p>
				<p>
					Usage of the tool:
					</p><pre class="screen">
		
$ opennlp LemmatizerTrainerME
Usage: opennlp LemmatizerTrainerME [-factory factoryName] [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding charsetName]

Arguments description:
	-factory factoryName
		A sub-class of LemmatizerFactory where to get implementation and resources.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
	encoding for reading and writing text, if absent the system default is used.
		
		</pre><p>
					Its now assumed that the english lemmatizer model should be trained
					from a file called
					en-lemmatizer.train which is encoded as UTF-8. The following command will train the
					lemmatizer and write the model to en-lemmatizer.bin:
					</p><pre class="screen">
		
$ opennlp LemmatizerTrainerME -model en-lemmatizer.bin -params PerceptronTrainerParams.txt -lang en -data en-lemmatizer.train -encoding UTF-8
		</pre><p>
				</p>
			</div>
			<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.lemmatizer.training.api"></a>Training API</h3></div></div></div>
				
				<p>
					The Lemmatizer offers an API to train a new lemmatizer model. First
					a training parameters
					file needs to be instantiated:
					</p><pre class="programlisting">
                    
 TrainingParameters mlParams = CmdLineUtil.loadTrainingParameters(params.getParams(), false);
 <b class="hl-keyword">if</b> (mlParams == null) {
   mlParams = ModelUtil.createDefaultTrainingParameters();
 }

                </pre><p>
					Then we read the training data:
					</p><pre class="programlisting">
                    
InputStreamFactory inputStreamFactory = null;
    <b class="hl-keyword">try</b> {
      inputStreamFactory = <b class="hl-keyword">new</b> MarkableFileInputStreamFactory(
          <b class="hl-keyword">new</b> File(en-lemmatizer.train));
    } <b class="hl-keyword">catch</b> (FileNotFoundException e) {
      e.printStackTrace();
    }
    ObjectStream&lt;String&gt; lineStream = null;
    LemmaSampleStream lemmaStream = null;
    <b class="hl-keyword">try</b> {
      lineStream = <b class="hl-keyword">new</b> PlainTextByLineStream(
      (inputStreamFactory), <b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
      lemmaStream = <b class="hl-keyword">new</b> LemmaSampleStream(lineStream);
    } <b class="hl-keyword">catch</b> (IOException e) {
      CmdLineUtil.handleCreateObjectStreamError(e);
    }

                </pre><p>
					The following step proceeds to train the model:
					</p><pre class="programlisting">
    LemmatizerModel model;
    try {
      LemmatizerFactory lemmatizerFactory = LemmatizerFactory
          .create(params.getFactory());
      model = LemmatizerME.train(params.getLang(), lemmaStream, mlParams,
          lemmatizerFactory);
    } catch (IOException e) {
      throw new TerminateToolException(-1,
          "IO error while reading training data or indexing data: "
              + e.getMessage(),
          e);
    } finally {
      try {
        sampleStream.close();
      } catch (IOException e) {
      }
    }
		</pre><p>
				</p>
			</div>
			</div>
			<div class="section" title="Lemmatizer Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.lemmatizer.evaluation"></a>Lemmatizer Evaluation</h2></div></div></div>
				
				<p>
					The built in evaluation can measure the accuracy of the statistical
					lemmatizer.
					The accuracy can be measured on a test data set.
				</p>
				<p>
					There is a command line tool to evaluate a given model on a test
					data set.
					The following command shows how the tool can be run:
					</p><pre class="screen">
				
$ opennlp LemmatizerEvaluator -model en-lemmatizer.bin -data en-lemmatizer.test -encoding utf-8
			 </pre><p>
					This will display the resulting accuracy score, e.g.:
					</p><pre class="screen">
				
Loading model ... done
Evaluating ... done

Accuracy: 0.9659110277825124
			 </pre><p>
				</p>
		</div>
</div>
	<div class="chapter" title="Chapter&nbsp;8.&nbsp;Chunker"><div class="titlepage"><div><div><h2 class="title"><a name="tools.chunker"></a>Chapter&nbsp;8.&nbsp;Chunker</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.parser.chunking">Chunking</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.chunking.cmdline">Chunker Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.chunking.api">Chunking API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.chunker.training">Chunker Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.chunker.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.chunker.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.chunker.evaluation">Chunker Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.chunker.evaluation.tool">Chunker Evaluation Tool</a></span></dt></dl></dd></dl></div>

	
	<div class="section" title="Chunking"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.parser.chunking"></a>Chunking</h2></div></div></div>
		
		<p>
		Text chunking consists of dividing a text in syntactically correlated parts of words,
		like noun groups, verb groups, but does not specify their internal structure, nor their role in the main sentence. 
		</p>
		
		<div class="section" title="Chunker Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.chunking.cmdline"></a>Chunker Tool</h3></div></div></div>
		
		<p>
		The easiest way to try out the Chunker is the command line tool. The tool is only intended
		for demonstration and testing.
		</p> 
		<p>
		Download the english maxent chunker model from the website and start the Chunker Tool with this command:
		</p>
		<p>
        </p><pre class="screen">
				
$ opennlp ChunkerME en-chunker.bin
		</pre><p>
		The Chunker now reads a pos tagged sentence per line from stdin.
		Copy these two sentences to the console: 
		</p><pre class="screen">
				
Rockwell_NNP International_NNP Corp._NNP 's_POS Tulsa_NNP unit_NN said_VBD it_PRP signed_VBD 
    a_DT tentative_JJ agreement_NN extending_VBG its_PRP$ contract_NN with_IN Boeing_NNP Co._NNP
    to_TO provide_VB structural_JJ parts_NNS for_IN Boeing_NNP 's_POS 747_CD jetliners_NNS ._.
Rockwell_NNP said_VBD the_DT agreement_NN calls_VBZ for_IN it_PRP to_TO supply_VB 200_CD
    additional_JJ so-called_JJ shipsets_NNS for_IN the_DT planes_NNS ._.
		</pre><p>
		The Chunker will now echo the sentences grouped tokens to the console:
				</p><pre class="screen">
				
[NP Rockwell_NNP International_NNP Corp._NNP ] [NP 's_POS Tulsa_NNP unit_NN ] [VP said_VBD ]
    [NP it_PRP ] [VP signed_VBD ] [NP a_DT tentative_JJ agreement_NN ] [VP extending_VBG ]
    [NP its_PRP$ contract_NN ] [PP with_IN ] [NP Boeing_NNP Co._NNP ] [VP to_TO provide_VB ]
    [NP structural_JJ parts_NNS ] [PP for_IN ] [NP Boeing_NNP ] [NP 's_POS 747_CD jetliners_NNS ] ._.
[NP Rockwell_NNP ] [VP said_VBD ] [NP the_DT agreement_NN ] [VP calls_VBZ ] [SBAR for_IN ]
    [NP it_PRP ] [VP to_TO supply_VB ] [NP 200_CD additional_JJ so-called_JJ shipsets_NNS ]
    [PP for_IN ] [NP the_DT planes_NNS ] ._.
		</pre><p>
		The tag set used by the english pos model is the <a class="ulink" href="http://www.cis.upenn.edu/~treebank/" target="_top">Penn Treebank tag set</a>.
		</p>
		</div>
		<div class="section" title="Chunking API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.chunking.api"></a>Chunking API</h3></div></div></div>
		
		<p>
		    The Chunker can be embedded into an application via its API.
			First the chunker model must be loaded into memory from disk or an other source.
			In the sample below its loaded from disk.
			</p><pre class="programlisting">
				
InputStream modelIn = null;
ChunkerModel model = null;

<b class="hl-keyword">try</b> {
  modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-chunker.bin"</i></b>);
  model = <b class="hl-keyword">new</b> ChunkerModel(modelIn);
} <b class="hl-keyword">catch</b> (IOException e) {
  <i class="hl-comment" style="color: silver">// Model loading failed, handle the error</i>
  e.printStackTrace();
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    } <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
			</pre><p>
			After the model is loaded a Chunker can be instantiated.
			</p><pre class="programlisting">
				
ChunkerME chunker = <b class="hl-keyword">new</b> ChunkerME(model);
			</pre><p>
			The Chunker instance is now ready to tag data. It expects a tokenized sentence
			as input, which is represented as a String array, each String object in the array
			is one token, and the POS tags associated with each token.
	   </p>
	   <p>
	   The following code shows how to determine the most likely chunk tag sequence for a sentence.
	   	</p><pre class="programlisting">
		  
String sent[] = <b class="hl-keyword">new</b> String[] { <b class="hl-string"><i style="color:red">"Rockwell"</i></b>, <b class="hl-string"><i style="color:red">"International"</i></b>, <b class="hl-string"><i style="color:red">"Corp."</i></b>, <b class="hl-string"><i style="color:red">"'s"</i></b>,
    <b class="hl-string"><i style="color:red">"Tulsa"</i></b>, <b class="hl-string"><i style="color:red">"unit"</i></b>, <b class="hl-string"><i style="color:red">"said"</i></b>, <b class="hl-string"><i style="color:red">"it"</i></b>, <b class="hl-string"><i style="color:red">"signed"</i></b>, <b class="hl-string"><i style="color:red">"a"</i></b>, <b class="hl-string"><i style="color:red">"tentative"</i></b>, <b class="hl-string"><i style="color:red">"agreement"</i></b>,
    <b class="hl-string"><i style="color:red">"extending"</i></b>, <b class="hl-string"><i style="color:red">"its"</i></b>, <b class="hl-string"><i style="color:red">"contract"</i></b>, <b class="hl-string"><i style="color:red">"with"</i></b>, <b class="hl-string"><i style="color:red">"Boeing"</i></b>, <b class="hl-string"><i style="color:red">"Co."</i></b>, <b class="hl-string"><i style="color:red">"to"</i></b>,
    <b class="hl-string"><i style="color:red">"provide"</i></b>, <b class="hl-string"><i style="color:red">"structural"</i></b>, <b class="hl-string"><i style="color:red">"parts"</i></b>, <b class="hl-string"><i style="color:red">"for"</i></b>, <b class="hl-string"><i style="color:red">"Boeing"</i></b>, <b class="hl-string"><i style="color:red">"'s"</i></b>, <b class="hl-string"><i style="color:red">"747"</i></b>,
    <b class="hl-string"><i style="color:red">"jetliners"</i></b>, <b class="hl-string"><i style="color:red">"."</i></b> };

String pos[] = <b class="hl-keyword">new</b> String[] { <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"POS"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>,
    <b class="hl-string"><i style="color:red">"VBD"</i></b>, <b class="hl-string"><i style="color:red">"PRP"</i></b>, <b class="hl-string"><i style="color:red">"VBD"</i></b>, <b class="hl-string"><i style="color:red">"DT"</i></b>, <b class="hl-string"><i style="color:red">"JJ"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>, <b class="hl-string"><i style="color:red">"VBG"</i></b>, <b class="hl-string"><i style="color:red">"PRP$"</i></b>, <b class="hl-string"><i style="color:red">"NN"</i></b>, <b class="hl-string"><i style="color:red">"IN"</i></b>,
    <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"TO"</i></b>, <b class="hl-string"><i style="color:red">"VB"</i></b>, <b class="hl-string"><i style="color:red">"JJ"</i></b>, <b class="hl-string"><i style="color:red">"NNS"</i></b>, <b class="hl-string"><i style="color:red">"IN"</i></b>, <b class="hl-string"><i style="color:red">"NNP"</i></b>, <b class="hl-string"><i style="color:red">"POS"</i></b>, <b class="hl-string"><i style="color:red">"CD"</i></b>, <b class="hl-string"><i style="color:red">"NNS"</i></b>,
    <b class="hl-string"><i style="color:red">"."</i></b> };

String tag[] = chunker.chunk(sent, pos);
			</pre><p>
			The tags array contains one chunk tag for each token in the input array. The corresponding
			tag can be found at the same index as the token has in the input array.
			The confidence scores for the returned tags can be easily retrieved from
			a ChunkerME with the following method call:
				   	</p><pre class="programlisting">
		  
<b class="hl-keyword">double</b> probs[] = chunker.probs();
			</pre><p>
			The call to probs is stateful and will always return the probabilities of the last
			tagged sentence. The probs method should only be called when the tag method
			was called before, otherwise the behavior is undefined.
			</p>
			<p>
			Some applications need to retrieve the n-best chunk tag sequences and not
			only the best sequence.
			The topKSequences method is capable of returning the top sequences.
			It can be called in a similar way as chunk.
			</p><pre class="programlisting">
		  
Sequence topSequences[] = chunk.topKSequences(sent, pos);
			</pre><p>	
			Each Sequence object contains one sequence. The sequence can be retrieved
			via Sequence.getOutcomes() which returns a tags array 
			and Sequence.getProbs() returns the probability array for this sequence.
	  		 </p>
		</div>
	</div>
	<div class="section" title="Chunker Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.chunker.training"></a>Chunker Training</h2></div></div></div>
		
		<p>
		The pre-trained models might not be available for a desired language,
		can not detect important entities or the performance is not good enough outside the news domain.
		</p>
		<p>
		These are the typical reason to do custom training of the chunker on a ne
	    corpus or on a corpus which is extended by private training data taken from the data which should be analyzed.
		</p>
		<p>
		The training data can be converted to the OpenNLP chunker training format,
		that is based on <a class="ulink" href="http://www.cnts.ua.ac.be/conll2000/chunking" target="_top">CoNLL2000</a>.
        Other formats may also be available.
		The train data consist of three columns separated one single space. Each word has been put on a
		separate line and there is an empty line after each sentence. The first column contains
		the current word, the second its part-of-speech tag and the third its chunk tag. 
		The chunk tags contain the name of the chunk type, for example I-NP for noun phrase words
		and I-VP for verb phrase words. Most chunk types have two types of chunk tags,
		B-CHUNK for the first word of the chunk and I-CHUNK for each other word in the chunk.
		Here is an example of the file format:
		</p>
		<p>
		Sample sentence of the training data: 
		</p><pre class="screen">
				
He        PRP  B-NP
reckons   VBZ  B-VP
the       DT   B-NP
current   JJ   I-NP
account   NN   I-NP
deficit   NN   I-NP
will      MD   B-VP
narrow    VB   I-VP
to        TO   B-PP
only      RB   B-NP
#         #    I-NP
1.8       CD   I-NP
billion   CD   I-NP
in        IN   B-PP
September NNP  B-NP
.         .    O
		</pre><p>
		Note that for improved visualization the example above uses tabs instead of a single space as column separator.
		</p>
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.chunker.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
		OpenNLP has a command line tool which is used to train the models available from the
		model download page on various corpora.
		</p> 
		<p>
		    Usage of the tool:
            </p><pre class="screen">
				
$ opennlp ChunkerTrainerME
Usage: opennlp ChunkerTrainerME[.ad] [-params paramsFile] [-iterations num] [-cutoff num] \
               -model modelFile -lang language -data sampleData [-encoding charsetName]

Arguments description:
        -params paramsFile
                training parameters file.
        -iterations num
                number of training iterations, ignored if -params is used.
        -cutoff num
                minimal number of times a feature must be seen, ignored if -params is used.
        -model modelFile
                output model file.
        -lang language
                language which is being processed.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
		</pre><p>
		Its now assumed that the english chunker model should be trained from a file called
		en-chunker.train which is encoded as UTF-8. The following command will train the
		name finder and write the model to en-chunker.bin: 
		</p><pre class="screen">
		
$ opennlp ChunkerTrainerME -model en-chunker.bin -lang en -data en-chunker.train -encoding UTF-8
		</pre><p>
		Additionally its possible to specify the number of iterations, the cutoff and to overwrite
		all types in the training data with a single type.
		</p>
		</div>
        <div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.chunker.training.api"></a>Training API</h3></div></div></div>
		
            <p>
                The Chunker offers an API to train a new chunker model. The following sample code
                illustrates how to do it:
                </p><pre class="programlisting">
                    
Charset charset = Charset.forName(<b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
ObjectStream&lt;String&gt; lineStream =
    <b class="hl-keyword">new</b> PlainTextByLineStream(<b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-chunker.train"</i></b>),charset);
ObjectStream&lt;ChunkSample&gt; sampleStream = <b class="hl-keyword">new</b> ChunkSampleStream(lineStream);

ChunkerModel model;

<b class="hl-keyword">try</b> {
  model = ChunkerME.train(<b class="hl-string"><i style="color:red">"en"</i></b>, sampleStream,
      <b class="hl-keyword">new</b> DefaultChunkerContextGenerator(), TrainingParameters.defaultParams());
}
<b class="hl-keyword">finally</b> {
  sampleStream.close();
}

OutputStream modelOut = null;
<b class="hl-keyword">try</b> {
  modelOut = <b class="hl-keyword">new</b> BufferedOutputStream(<b class="hl-keyword">new</b> FileOutputStream(modelFile));
  model.serialize(modelOut);
} <b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelOut != null)
     modelOut.close();
}
                </pre><p>
            </p>
		</div>
	</div>
	
	<div class="section" title="Chunker Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.chunker.evaluation"></a>Chunker Evaluation</h2></div></div></div>
		
		<p>
		The built in evaluation can measure the chunker performance. The performance is either
		measured on a test dataset or via cross validation. 
		</p>
		<div class="section" title="Chunker Evaluation Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.chunker.evaluation.tool"></a>Chunker Evaluation Tool</h3></div></div></div>
		
		<p>
		    The following command shows how the tool can be run:
            </p><pre class="screen">
				
$ opennlp ChunkerEvaluator
Usage: opennlp ChunkerEvaluator[.ad] -model model [-misclassified true|false] \
               [-detailedF true|false] -lang language -data sampleData [-encoding charsetName]
		</pre><p>
		A sample of the command considering you have a data sample named en-chunker.eval
		and you trained a model called en-chunker.bin:
            </p><pre class="screen">
				
$ opennlp ChunkerEvaluator -model en-chunker.bin -data en-chunker.eval -encoding UTF-8
		</pre><p>
		and here is a sample output:  
		</p><pre class="screen">
		
Precision: 0.9255923572240226
Recall: 0.9220610430991112
F-Measure: 0.9238233255623465
		</pre><p>
		You can also use the tool to perform 10-fold cross validation of the Chunker.
he following command shows how the tool can be run:
        </p><pre class="screen">
				
$ opennlp ChunkerCrossValidator
Usage: opennlp ChunkerCrossValidator[.ad] [-params paramsFile] [-iterations num] [-cutoff num] \
               [-misclassified true|false] [-folds num] [-detailedF true|false] \
               -lang language -data sampleData [-encoding charsetName]

Arguments description:
        -params paramsFile
                training parameters file.
        -iterations num
                number of training iterations, ignored if -params is used.
        -cutoff num
                minimal number of times a feature must be seen, ignored if -params is used.
        -misclassified true|false
                if true will print false negatives and false positives.
        -folds num
                number of folds, default is 10.
        -detailedF true|false
                if true will print detailed FMeasure results.
        -lang language
                language which is being processed.
        -data sampleData
                data to be used, usually a file name.
        -encoding charsetName
                encoding for reading and writing text, if absent the system default is used.
		</pre><p>
		It is not necessary to pass a model. The tool will automatically split the data to train and evaluate:
        </p><pre class="screen">
            
$ opennlp ChunkerCrossValidator -lang pt -data en-chunker.cross -encoding UTF-8
		</pre><p>
		</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;9.&nbsp;Parser"><div class="titlepage"><div><div><h2 class="title"><a name="tools.parser"></a>Chapter&nbsp;9.&nbsp;Parser</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.parser.parsing">Parsing</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.parsing.cmdline">Parser Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.parsing.api">Parsing API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.parser.training">Parser Training</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.training.tool">Training Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.training.api">Training API</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.parser.evaluation">Parser Evaluation</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.parser.evaluation.tool">Parser Evaluation Tool</a></span></dt><dt><span class="section"><a href="#tools.parser.evaluation.api">Evaluation API</a></span></dt></dl></dd></dl></div>

	
	<div class="section" title="Parsing"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.parser.parsing"></a>Parsing</h2></div></div></div>
		
		<p>
		</p>
		
		<div class="section" title="Parser Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.parsing.cmdline"></a>Parser Tool</h3></div></div></div>
		
		<p>
		The easiest way to try out the Parser is the command line tool.
		The tool is only intended for demonstration and testing.
		Download the English chunking parser model from the our website and start the Parse
 		Tool with the following command.
				</p><pre class="screen">
				
$ opennlp Parser en-parser.bin en-parser-chunking.bin
		</pre><p>
		Loading the big parser model can take several seconds, be patient.
		Copy this sample sentence to the console.
		</p><pre class="screen">
				
The quick brown fox jumps over the lazy dog .
		</pre><p>
		The parser should now print the following to the console.
				</p><pre class="screen">
				
(TOP (NP (NP (DT The) (JJ quick) (JJ brown) (NN fox) (NNS jumps)) (PP (IN over) (NP (DT the)
    (JJ lazy) (NN dog))) (. .)))
		</pre><p>
		With the following command the input can be read from a file and be written to an output file.
				</p><pre class="screen">
				
$ opennlp Parser en-parser.bin en-parser-chunking.bin &lt; article-tokenized.txt &gt; article-parsed.txt.
		</pre><p>
		The article-tokenized.txt file must contain one sentence per line which is
		tokenized with the English tokenizer model from our website.
		See the Tokenizer documentation for further details.
		</p>
		</div>
		<div class="section" title="Parsing API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.parsing.api"></a>Parsing API</h3></div></div></div>
		
		<p>
			The Parser can be easily integrated into an application via its API.
			To instantiate a Parser the parser model must be loaded first.
			</p><pre class="programlisting">
				
InputStream modelIn = <b class="hl-keyword">new</b> FileInputStream(<b class="hl-string"><i style="color:red">"en-parser-chunking.bin"</i></b>);
<b class="hl-keyword">try</b> {
  ParserModel model = <b class="hl-keyword">new</b> ParserModel(modelIn);
}
<b class="hl-keyword">catch</b> (IOException e) {
  e.printStackTrace();
}
<b class="hl-keyword">finally</b> {
  <b class="hl-keyword">if</b> (modelIn != null) {
    <b class="hl-keyword">try</b> {
      modelIn.close();
    }
    <b class="hl-keyword">catch</b> (IOException e) {
    }
  }
}
		</pre><p>
		Unlike the other components to instantiate the Parser a factory method
		should be used instead of creating the Parser via the new operator.
		The parser model is either trained for the chunking parser or the tree
		insert parser the parser implementation must be chosen correctly.
		The factory method will read a type parameter from the model and create
		an instance of the corresponding parser implementation.
		</p><pre class="programlisting">
				
Parser parser = ParserFactory.create(model);
		</pre><p>
		Right now the tree insert parser is still experimental and there is no pre-trained model for it.
		The parser expect a whitespace tokenized sentence. A utility method from the command
		line tool can parse the sentence String. The following code shows how the parser can be called.
				</p><pre class="programlisting">
				
String sentence = <b class="hl-string"><i style="color:red">"The quick brown fox jumps over the lazy dog ."</i></b>;
Parse topParses[] = ParserTool.parseLine(sentence, parser, <span class="hl-number">1</span>);
		</pre><p>
		
		The topParses array only contains one parse because the number of parses is set to 1.
		The Parse object contains the parse tree.
		To display the parse tree call the show method. It either prints the parse to
		the console or into a provided StringBuffer. Similar to Exception.printStackTrace.
		</p>
		<p>
		TODO: Extend this section with more information about the Parse object.
		</p>
		</div>
	</div>
	<div class="section" title="Parser Training"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.parser.training"></a>Parser Training</h2></div></div></div>
		
		<p>
				The OpenNLP offers two different parser implementations, the chunking parser and the
		treeinsert parser. The later one is still experimental and not recommended for production use.
		(TODO: Add a section which explains the two different approaches)
		The training can either be done with the command line tool or the training API.
		In the first case the training data must be available in the OpenNLP format. Which is
		the Penn Treebank format, but with the limitation of a sentence per line.
		</p><pre class="programlisting">
				
(TOP (S (NP-SBJ (DT Some) )(VP (VBP say) (NP (NNP November) ))(. .) ))
(TOP (S (NP-SBJ (PRP I) )(VP (VBP say) (NP (CD 1992) ))(. .) ('' '') ))
		</pre><p>
		Penn Treebank annotation guidelines can be found on the
            <a class="ulink" href="http://www.cis.upenn.edu/~treebank/" target="_top">Penn Treebank home page</a>.
		A parser model also contains a pos tagger model, depending on the amount of available
		training data it is recommend to switch the tagger model against a tagger model which
		was trained on a larger corpus. The pre-trained parser model provided on the website
		is doing this to achieve a better performance. (TODO: On which data is the model on
		the website trained, and say on which data the tagger model is trained)
		</p>
		<div class="section" title="Training Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.training.tool"></a>Training Tool</h3></div></div></div>
		
		<p>
		OpenNLP has a command line tool which is used to train the models available from the
		model download page on various corpora. The data must be converted to the OpenNLP parser
		training format, which is shortly explained above.
		To train the parser a head rules file is also needed. (TODO: Add documentation about the head rules file)
		Usage of the tool:
            </p><pre class="screen">
				
$ opennlp ParserTrainer
Usage: opennlp ParserTrainer -headRules headRulesFile [-parserType CHUNKING|TREEINSERT] \
                             [-params paramsFile] [-iterations num] [-cutoff num] \
                             -model modelFile -lang language -data sampleData \
                             [-encoding charsetName]

Arguments description:
	-headRules headRulesFile
		head rules file.
	-parserType CHUNKING|TREEINSERT
		one of CHUNKING or TREEINSERT, default is CHUNKING.
	-params paramsFile
		training parameters file.
	-iterations num
		number of training iterations, ignored if -params is used.
	-cutoff num
		minimal number of times a feature must be seen, ignored if -params is used.
	-model modelFile
		output model file.
	-format formatName
		data format, might have its own parameters.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.
        </pre><p>
		The model on the website was trained with the following command:
		</p><pre class="screen">
		
$ opennlp ParserTrainer -model en-parser-chunking.bin -parserType CHUNKING \
                        -head-rules head_rules \
                        -lang en -data train.all -encoding ISO-8859-1
    
		</pre><p>
		Its also possible to specify the cutoff and the number of iterations, these parameters
		are used for all trained models. The -parserType parameter is an optional parameter,
		to use the tree insertion parser, specify TREEINSERT as type. The TaggerModelReplacer
		tool replaces the tagger model inside the parser model with a new one. 
		</p>
		<p>
		Note: The original parser model will be overwritten with the new parser model which
		contains the replaced tagger model.
        </p><pre class="screen">
		
$ opennlp TaggerModelReplacer en-parser-chunking.bin en-pos-maxent.bin
		</pre><p>
		Additionally there are tools to just retrain the build or the check model.
		</p>
		</div>

		<div class="section" title="Training API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.training.api"></a>Training API</h3></div></div></div>
		  
		  <p>
		The Parser training API supports the training of a new parser model.
		Four steps are necessary to train it:
		</p><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem">
			  <p>A HeadRules class needs to be instantiated: currently EnglishHeadRules and AncoraSpanishHeadRules are available.</p>
			</li><li class="listitem">
				<p>The application must open a sample data stream.</p>
			</li><li class="listitem">
				<p>Call a Parser train method: This can be either the CHUNKING or the TREEINSERT parser.</p>
			</li><li class="listitem">
				<p>Save the ParseModel to a file or database.</p>
			</li></ul></div><p>
		The following code snippet shows how to instantiate the HeadRules:
		</p><pre class="programlisting">
				
<b class="hl-keyword">static</b> HeadRules createHeadRules(TrainerToolParams params) <b class="hl-keyword">throws</b> IOException {

    ArtifactSerializer headRulesSerializer = null;

    <b class="hl-keyword">if</b> (params.getHeadRulesSerializerImpl() != null) {
      headRulesSerializer = ExtensionLoader.instantiateExtension(ArtifactSerializer.<b class="hl-keyword">class</b>,
              params.getHeadRulesSerializerImpl());
    }
    <b class="hl-keyword">else</b> {
      <b class="hl-keyword">if</b> (<b class="hl-string"><i style="color:red">"en"</i></b>.equals(params.getLang())) {
        headRulesSerializer = <b class="hl-keyword">new</b> opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
      }
      <b class="hl-keyword">else</b> <b class="hl-keyword">if</b> (<b class="hl-string"><i style="color:red">"es"</i></b>.equals(params.getLang())) {
        headRulesSerializer = <b class="hl-keyword">new</b> opennlp.tools.parser.lang.es.AncoraSpanishHeadRules.HeadRulesSerializer();
      }
      <b class="hl-keyword">else</b> {
        <i class="hl-comment" style="color: silver">// default for now, this case should probably cause an error ...</i>
        headRulesSerializer = <b class="hl-keyword">new</b> opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
      }
    }

    Object headRulesObject = headRulesSerializer.create(<b class="hl-keyword">new</b> FileInputStream(params.getHeadRules()));

    <b class="hl-keyword">if</b> (headRulesObject <b class="hl-keyword">instanceof</b> HeadRules) {
      <b class="hl-keyword">return</b> (HeadRules) headRulesObject;
    }
    <b class="hl-keyword">else</b> {
      <b class="hl-keyword">throw</b> <b class="hl-keyword">new</b> TerminateToolException(-<span class="hl-number">1</span>, <b class="hl-string"><i style="color:red">"HeadRules Artifact Serializer must create an object of type HeadRules!"</i></b>);
    }
}
	</pre><p>
		The following code illustrates the three other steps, namely, opening the data, training
	the model and saving the ParserModel into an output file.
		</p><pre class="programlisting">
				
ParserModel model = null;
File modelOutFile = params.getModel();
CmdLineUtil.checkOutputFile(<b class="hl-string"><i style="color:red">"parser model"</i></b>, modelOutFile);

<b class="hl-keyword">try</b> {
  HeadRules rules = createHeadRules(params);
  InputStreamFactory inputStreamFactory = <b class="hl-keyword">new</b> MarkableFileInputStreamFactory(<b class="hl-keyword">new</b> File(<b class="hl-string"><i style="color:red">"parsing.train"</i></b>));
  ObjectStream&lt;String&gt; stringStream = <b class="hl-keyword">new</b> PlainTextByLineStream(inputStreamFactory, <b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
  ObjectStream&lt;Parse&gt; sampleStream = <b class="hl-keyword">new</b> ParseSample(stringStream);

  ParserType type = parseParserType(params.getParserType());
  <b class="hl-keyword">if</b> (ParserType.CHUNKING.equals(type)) {
    model = opennlp.tools.parser.chunking.Parser.train(
            params.getLang(), sampleStream, rules,
            mlParams);
  } <b class="hl-keyword">else</b> <b class="hl-keyword">if</b> (ParserType.TREEINSERT.equals(type)) {
    model = opennlp.tools.parser.treeinsert.Parser.train(params.getLang(), sampleStream, rules,
            mlParams);
    }
  }
  <b class="hl-keyword">catch</b> (IOException e) {
      <b class="hl-keyword">throw</b> <b class="hl-keyword">new</b> TerminateToolException(-<span class="hl-number">1</span>, <b class="hl-string"><i style="color:red">"IO error while reading training data or indexing data: "</i></b>
          + e.getMessage(), e);
  }
  <b class="hl-keyword">finally</b> {
      <b class="hl-keyword">try</b> {
        sampleStream.close();
      }
      <b class="hl-keyword">catch</b> (IOException e) {
        <i class="hl-comment" style="color: silver">// sorry that this can fail</i>
      }
  }
  CmdLineUtil.writeModel(<b class="hl-string"><i style="color:red">"parser"</i></b>, modelOutFile, model);

	</pre><p>
		</p>
		</div>
	</div>
	<div class="section" title="Parser Evaluation"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.parser.evaluation"></a>Parser Evaluation</h2></div></div></div>
		
		<p>
			The built in evaluation can measure the parser performance. The
			performance is measured
			on a test dataset.
		</p>
		<div class="section" title="Parser Evaluation Tool"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.evaluation.tool"></a>Parser Evaluation Tool</h3></div></div></div>
			
			<p>
				The following command shows how the tool can be run:
				</p><pre class="screen">
				
$ opennlp ParserEvaluator
Usage: opennlp ParserEvaluator[.ontonotes|frenchtreebank] [-misclassified true|false] -model model \
               -data sampleData [-encoding charsetName]
		</pre><p>
				A sample of the command considering you have a data sample named
				en-parser-chunking.eval
				and you trained a model called en-parser-chunking.bin:
				</p><pre class="screen">
				
$ opennlp ParserEvaluator -model en-parser-chunking.bin -data en-parser-chunking.eval -encoding UTF-8
		</pre><p>
				and here is a sample output:
				</p><pre class="screen">
		
Precision: 0.9009744742967609
Recall: 0.8962012400910446
F-Measure: 0.8985815184245214
		</pre><p>
			</p>
			<p>
				The Parser Evaluation tool reimplements the PARSEVAL scoring method
				as implemented by the
				<a class="ulink" href="http://nlp.cs.nyu.edu/evalb/" target="_top">EVALB</a>
				script, which is the most widely used evaluation
				tool for constituent parsing. Note however that currently the Parser
				Evaluation tool does not allow
				to make exceptions in the constituents to be evaluated, in the way
				Collins or Bikel usually do. Any
				contributions are very welcome. If you want to contribute please contact us on
				the mailing list or comment
				on the jira issue
				<a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-688" target="_top">OPENNLP-688</a>.
			</p>
		</div>
		<div class="section" title="Evaluation API"><div class="titlepage"><div><div><h3 class="title"><a name="tools.parser.evaluation.api"></a>Evaluation API</h3></div></div></div>
		
		<p>
		The evaluation can be performed on a pre-trained model and a test dataset or via cross validation.
		In the first case the model must be loaded and a Parse ObjectStream must be created (see code samples above),
		assuming these two objects exist the following code shows how to perform the evaluation:
			</p><pre class="programlisting">
			  
Parser parser = ParserFactory.create(model);
ParserEvaluator evaluator = <b class="hl-keyword">new</b> ParserEvaluator(parser);
evaluator.evaluate(sampleStream);

FMeasure result = evaluator.getFMeasure();

System.out.println(result.toString());
			</pre><p>
			In the cross validation case all the training arguments must be
			provided (see the Training API section above).
			To perform cross validation the ObjectStream must be resettable.
			</p><pre class="programlisting">
				
InputStreamFactory inputStreamFactory = <b class="hl-keyword">new</b> MarkableFileInputStreamFactory(<b class="hl-keyword">new</b> File(<b class="hl-string"><i style="color:red">"parsing.train"</i></b>));
ObjectStream&lt;String&gt; stringStream = <b class="hl-keyword">new</b> PlainTextByLineStream(inputStreamFactory, <b class="hl-string"><i style="color:red">"UTF-8"</i></b>);
ObjectStream&lt;Parse&gt; sampleStream = <b class="hl-keyword">new</b> ParseSample(stringStream);
ParserCrossValidator evaluator = <b class="hl-keyword">new</b> ParserCrossValidator(<b class="hl-string"><i style="color:red">"en"</i></b>, trainParameters, headRules, \
parserType, listeners.toArray(<b class="hl-keyword">new</b> ParserEvaluationMonitor[listeners.size()])));
evaluator.evaluate(sampleStream, <span class="hl-number">10</span>);

FMeasure result = evaluator.getFMeasure();

System.out.println(result.toString());
			</pre><p>
		</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;10.&nbsp;Coreference Resolution"><div class="titlepage"><div><div><h2 class="title"><a name="tools.coref"></a>Chapter&nbsp;10.&nbsp;Coreference Resolution</h2></div></div></div>

<p>
The OpenNLP Coreference Resolution system links multiple mentions of an
entity in a document together.
The OpenNLP implementation is currently limited to noun phrase mentions,
other mention types cannot be resolved.
</p>

<p>
TODO: Write more documentation about the coref component. Any contributions
are very welcome. If you want to contribute please contact us on the mailing list
or comment on the jira issue <a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-48" target="_top">OPENNLP-48</a>.
</p>
 
</div>
	<div class="chapter" title="Chapter&nbsp;11.&nbsp;Extending OpenNLP"><div class="titlepage"><div><div><h2 class="title"><a name="tools.extension"></a>Chapter&nbsp;11.&nbsp;Extending OpenNLP</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.extension.writing">Writing an extension</a></span></dt><dt><span class="section"><a href="#tools.extension.osgi">Running in an OSGi container</a></span></dt></dl></div>

<p>
In OpenNLP extension can be used to add new functionality and to
heavily customize an existing component. Most components define
a factory class which can be implemented to customize the creation
of it. And some components allow to add new feature generators.
</p>

<div class="section" title="Writing an extension"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.extension.writing"></a>Writing an extension</h2></div></div></div>
	
	<p>
	In many places it is possible to pass in an extension class name to customize
	some aspect of OpenNLP. The implementation class needs to implement the specified
	interface and should have a public no-argument constructor.
	</p>
</div>

<div class="section" title="Running in an OSGi container"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.extension.osgi"></a>Running in an OSGi container</h2></div></div></div>
	
	<p>
	The traditional way of loading an extension via Class.forName does not work
	in an OSGi environment because the class paths of the OpenNLP Tools and extension
	bundle are isolated. OSGi uses services to provide functionality from one bundle
	to another. The extension bundle must register its extensions as services so that
	the OpenNLP tools bundle can use them.
	The following code illustrates how that can be done:
				</p><pre class="programlisting">
				
Dictionary&lt;String, String&gt; props = <b class="hl-keyword">new</b> Hashtable&lt;String, String&gt;();
props.put(ExtensionServiceKeys.ID, <b class="hl-string"><i style="color:red">"org.test.SuperTokenizer"</i></b>);
context.registerService(Tokenizer.<b class="hl-keyword">class</b>.getName(), <b class="hl-keyword">new</b> org.test.SuperTokenizer(), props);
		</pre><p>
	The service OpenNLP is looking for might not be (yet) available. In this case OpenNLP
	waits until a timeout is reached. If loading the extension fails an ExtensionNotLoadedException
	is thrown. This exception is also thrown when the thread is interrupted while it is waiting for the
	extension, the interrupted flag will be set again and the calling code has a chance to handle it.
	</p>
</div>
</div>	
	<div class="chapter" title="Chapter&nbsp;12.&nbsp;Corpora"><div class="titlepage"><div><div><h2 class="title"><a name="tools.corpora"></a>Chapter&nbsp;12.&nbsp;Corpora</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.corpora.conll">CONLL</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.conll.2000">CONLL 2000</a></span></dt><dt><span class="section"><a href="#tools.corpora.conll.2002">CONLL 2002</a></span></dt><dt><span class="section"><a href="#tools.corpora.conll.2003">CONLL 2003</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.arvores-deitadas">Arvores Deitadas</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.getting">Getting the data</a></span></dt><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.converting">Converting the data (optional)</a></span></dt><dt><span class="section"><a href="#tools.corpora.arvores-deitadas.evaluation">Training and Evaluation</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.leipzig">Leipzig Corpora</a></span></dt><dt><span class="section"><a href="#tools.corpora.ontonotes">OntoNotes Release 4.0</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.ontonotes.namefinder">Name Finder Training</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.corpora.brat">Brat Format Support</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.corpora.brat.webtool">Sentences and Tokens</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.training">Training</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.evaluation">Evaluation</a></span></dt><dt><span class="section"><a href="#tools.corpora.brat.cross-validation">Cross Validation</a></span></dt></dl></dd></dl></div>

	
	<p>
	    OpenNLP has built-in support to convert into the native training format or directly use
        various corpora	needed by the different	trainable components.
	</p>
	<div class="section" title="CONLL"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.corpora.conll"></a>CONLL</h2></div></div></div>
		
		<p>
		CoNLL stands for the Conference on Computational Natural Language Learning and is not
		a single project but a consortium of developers attempting to broaden the computing
		environment. More information about the entire conference series can be obtained here
		for CoNLL.
		</p>
		<div class="section" title="CONLL 2000"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.conll.2000"></a>CONLL 2000</h3></div></div></div>
		
		<p>
		The shared task of CoNLL-2000 is Chunking.
		</p>
		<div class="section" title="Getting the data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2000.getting"></a>Getting the data</h4></div></div></div>
		
		<p>
		CoNLL-2000 made available training and test data for the Chunk task in English. 
		The data consists of the same partitions of the Wall Street Journal corpus (WSJ) 
		as the widely used data for noun phrase chunking: sections 15-18 as training data 
		(211727 tokens) and section 20 as test data (47377 tokens). The annotation of the 
		data has been derived from the WSJ corpus by a program written by Sabine Buchholz 
		from Tilburg University, The Netherlands. Both training and test data can be
		obtained from <a class="ulink" href="http://www.cnts.ua.ac.be/conll2000/chunking" target="_top">http://www.cnts.ua.ac.be/conll2000/chunking</a>. 
		</p>
		</div>
		<div class="section" title="Converting the data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2000.converting"></a>Converting the data</h4></div></div></div>
		
		<p>
		The data don't need to be transformed because Apache OpenNLP Chunker follows
		the CONLL 2000 format for training. Check <a class="link" href="#tools.chunker.training" title="Chunker Training">Chunker Training</a> section to learn more.
		</p>
		</div>
		<div class="section" title="Training"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2000.training"></a>Training</h4></div></div></div>
		
		<p>
		 We can train the model for the Chunker using the train.txt available at CONLL 2000:
		 </p><pre class="screen">
			
$ opennlp ChunkerTrainerME -model en-chunker.bin -iterations 500 \
                           -lang en -data train.txt -encoding UTF-8
		</pre><p>
		</p><pre class="screen">
			
Indexing events using cutoff of 5

	Computing event counts...  done. 211727 events
	Indexing...  done.
Sorting and merging events... done. Reduced 211727 events to 197252.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 197252
	    Number of Outcomes: 22
	  Number of Predicates: 107838
...done.
Computing model parameters...
Performing 500 iterations.
  1:  .. loglikelihood=-654457.1455212828	0.2601510435608118
  2:  .. loglikelihood=-239513.5583724216	0.9260037690044255
  3:  .. loglikelihood=-141313.1386347238	0.9443387003074715
  4:  .. loglikelihood=-101083.50853437989	0.954375209585929
... cut lots of iterations ...
498:  .. loglikelihood=-1710.8874647317095	0.9995040783650645
499:  .. loglikelihood=-1708.0908900815848	0.9995040783650645
500:  .. loglikelihood=-1705.3045902366732	0.9995040783650645
Writing chunker model ... done (4.019s)

Wrote chunker model to path: .\en-chunker.bin
		</pre><p>
		</p>
		</div>
		<div class="section" title="Evaluating"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2000.evaluation"></a>Evaluating</h4></div></div></div>
		
		<p>
		We evaluate the model using the file test.txt  available at CONLL 2000:
		</p><pre class="screen">
			
$ opennlp ChunkerEvaluator -model en-chunker.bin -lang en -encoding utf8 -data test.txt
		</pre><p>
		</p><pre class="screen">
			
Loading Chunker model ... done (0,665s)
current: 85,8 sent/s avg: 85,8 sent/s total: 86 sent
current: 88,1 sent/s avg: 87,0 sent/s total: 174 sent
current: 156,2 sent/s avg: 110,0 sent/s total: 330 sent
current: 192,2 sent/s avg: 130,5 sent/s total: 522 sent
current: 167,2 sent/s avg: 137,8 sent/s total: 689 sent
current: 179,2 sent/s avg: 144,6 sent/s total: 868 sent
current: 183,2 sent/s avg: 150,3 sent/s total: 1052 sent
current: 183,2 sent/s avg: 154,4 sent/s total: 1235 sent
current: 169,2 sent/s avg: 156,0 sent/s total: 1404 sent
current: 178,2 sent/s avg: 158,2 sent/s total: 1582 sent
current: 172,2 sent/s avg: 159,4 sent/s total: 1754 sent
current: 177,2 sent/s avg: 160,9 sent/s total: 1931 sent


Average: 161,6 sent/s 
Total: 2013 sent
Runtime: 12.457s

Precision: 0.9244354736974896
Recall: 0.9216837162502096
F-Measure: 0.9230575441395671
		</pre><p>
		</p>
		</div>
	</div>
		<div class="section" title="CONLL 2002"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.conll.2002"></a>CONLL 2002</h3></div></div></div>
		
		<p>
		The shared task of CoNLL-2002 is language independent named entity recognition for Spanish and Dutch.
		</p>
		<div class="section" title="Getting the data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2002.getting"></a>Getting the data</h4></div></div></div>
		
		<p>The data consists of three files per language: one training file and two test files testa and testb.
		The first test file will be used in the development phase for finding good parameters for the learning system.
		The second test file will be used for the final evaluation. Currently there are data files available for two languages:
		Spanish and Dutch.
		</p>
		<p>
		The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are
		from May 2000. The annotation was carried out by the <a class="ulink" href="http://www.talp.cat/" target="_top">TALP Research Center</a> of the Technical University of Catalonia (UPC)
		and the <a class="ulink" href="http://clic.ub.edu/" target="_top">Center of Language and Computation (CLiC)</a>of the University of Barcelona (UB), and funded by the European Commission
		through the NAMIC project (IST-1999-12392). 
		</p>
		<p>
		The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1).
		The data was annotated as a part of the <a class="ulink" href="http://atranos.esat.kuleuven.ac.be/" target="_top">Atranos</a> project at the University of Antwerp. 
		</p>
		<p>
		You can find the Spanish files here: 
		<a class="ulink" href="http://www.lsi.upc.edu/~nlp/tools/nerc/nerc.html" target="_top">http://www.lsi.upc.edu/~nlp/tools/nerc/nerc.html</a>
		You must download esp.train.gz, unzip it and you will see the file esp.train.
		</p>
		<p>
		You can find the Dutch files here: 
		<a class="ulink" href="http://www.cnts.ua.ac.be/conll2002/ner.tgz" target="_top">http://www.cnts.ua.ac.be/conll2002/ner.tgz</a>
		You must unzip it and go to /ner/data/ned.train.gz, so you unzip it too, and you will see the file ned.train.
		</p>
		</div>
		<div class="section" title="Converting the data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2002.converting"></a>Converting the data</h4></div></div></div>
		
		<p>
		I will use Spanish data as reference, but it would be the same operations to Dutch. You just must remember change &#8220;-lang es&#8221; to &#8220;-lang nl&#8221; and use
		the correct training files. So to convert the information to the OpenNLP format: 
		</p><pre class="screen">
			
$ opennlp TokenNameFinderConverter conll02 -data esp.train -lang es -types per &gt; es_corpus_train_persons.txt
		</pre><p>
		Optionally, you can convert the training test samples as well.
		</p><pre class="screen">
			
$ opennlp TokenNameFinderConverter conll02 -data esp.testa -lang es -types per &gt; corpus_testa.txt
$ opennlp TokenNameFinderConverter conll02 -data esp.testb -lang es -types per &gt; corpus_testb.txt
		</pre><p>
		</p>
		</div>
		<div class="section" title="Training with Spanish data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2002.training.spanish"></a>Training with Spanish data</h4></div></div></div>
		
		<p>
		To train the model for the name finder: 
		</p><pre class="screen">
			
\bin\opennlp TokenNameFinderTrainer -lang es -encoding u
tf8 -iterations 500 -data es_corpus_train_persons.txt -model es_ner_person.bin


Indexing events using cutoff of 5

        Computing event counts...  done. 264715 events
        Indexing...  done.
Sorting and merging events... done. Reduced 264715 events to 222660.
Done indexing.
Incorporating indexed data for training...
done.
        Number of Event Tokens: 222660
           Number of Outcomes: 3
          Number of Predicates: 71514
...done.
Computing model parameters ...
Performing 500 iterations.
  1:  ... loglikelihood=-290819.1519958615      0.9689326256540053
  2:  ... loglikelihood=-37097.17676455632      0.9689326256540053
  3:  ... loglikelihood=-22910.372489660916     0.9706476776911017
  4:  ... loglikelihood=-17091.547325669497     0.9777874317662392
  5:  ... loglikelihood=-13797.620926769372     0.9833821279489262
  6:  ... loglikelihood=-11715.806710780415     0.9867140131839903
  7:  ... loglikelihood=-10289.222078246517     0.9886859452618855
  8:  ... loglikelihood=-9249.208318314624      0.9902310031543358
  9:  ... loglikelihood=-8454.169590899777      0.9913227433277298
 10:  ... loglikelihood=-7823.742997451327      0.9921953799369133
 11:  ... loglikelihood=-7309.375882641964      0.9928224694482746
 12:  ... loglikelihood=-6880.131972149693      0.9932946754056249
 13:  ... loglikelihood=-6515.3828767792365     0.993638441342576
 14:  ... loglikelihood=-6200.82723154046       0.9939595413935742
 15:  ... loglikelihood=-5926.213730444915      0.994269308501596
 16:  ... loglikelihood=-5683.9821840753275     0.9945299661900534
 17:  ... loglikelihood=-5468.4211798176075     0.9948246227074401
 18:  ... loglikelihood=-5275.127017232056      0.9950286156810154

... cut lots of iterations ...

491:  ... loglikelihood=-1174.8485558758211     0.998983812779782
492:  ... loglikelihood=-1173.9971776942477     0.998983812779782
493:  ... loglikelihood=-1173.1482915871768     0.998983812779782
494:  ... loglikelihood=-1172.3018855781158     0.998983812779782
495:  ... loglikelihood=-1171.457947774544      0.998983812779782
496:  ... loglikelihood=-1170.6164663670502     0.998983812779782
497:  ... loglikelihood=-1169.7774296286693     0.998983812779782
498:  ... loglikelihood=-1168.94082591387       0.998983812779782
499:  ... loglikelihood=-1168.1066436580463     0.9989875904274408
500:  ... loglikelihood=-1167.2748713765225     0.9989875904274408
Writing name finder model ... done (2,168s)

Wrote name finder model to
path: .\es_ner_person.bin
		</pre><p>
		</p>
		</div>
		</div>
		
		<div class="section" title="CONLL 2003"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.conll.2003"></a>CONLL 2003</h3></div></div></div>
		
		<p>
		The shared task of CoNLL-2003 is language independent named entity recognition
		for English and German.
		</p>
		<div class="section" title="Getting the data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2003.getting"></a>Getting the data</h4></div></div></div>
		
		<p>
		The English data is the Reuters Corpus, which is a collection of news wire articles.
		The Reuters Corpus can be obtained free of charges from the NIST for research
		purposes: <a class="ulink" href="http://trec.nist.gov/data/reuters/reuters.html" target="_top">http://trec.nist.gov/data/reuters/reuters.html</a>
		</p>
		<p>
		The German data is a collection of articles from the German newspaper Frankfurter
		Rundschau. The articles are part of the ECI Multilingual Text Corpus which
		can be obtained for 75$ (2010) from the Linguistic Data Consortium:
<a class="ulink" href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5" target="_top">http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5</a>		</p>
		<p>After one of the corpora is available the data must be
		transformed as explained in the README file to the CONLL format.
		The transformed data can be read by the OpenNLP CONLL03 converter.
		</p>
		</div>
		<div class="section" title="Converting the data (optional)"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2003.converting"></a>Converting the data (optional)</h4></div></div></div>
		
		<p>
		To convert the information to the OpenNLP format:
		</p><pre class="screen">
			
$ opennlp TokenNameFinderConverter conll03 -lang en -types per -data eng.train &gt; corpus_train.txt
		</pre><p>
		Optionally, you can convert the training test samples as well.
		</p><pre class="screen">
			
$ opennlp TokenNameFinderConverter conll03 -lang en -types per -data eng.testa &gt; corpus_testa.txt
$ opennlp TokenNameFinderConverter conll03 -lang en -types per -data eng.testb &gt; corpus_testb.txt
		</pre><p>
		</p>
		</div>
		<div class="section" title="Training with English data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2003.training.english"></a>Training with English data</h4></div></div></div>
		
            <p>
                You can train the model for the name finder this way:
                </p><pre class="screen">
                
$ opennlp TokenNameFinderTrainer.conll03 -model en_ner_person.bin -iterations 500 \
                                 -lang en -types per -data eng.train -encoding utf8
                </pre><p>
            </p>
		    <p>
                If you have converted the data, then you can train the model for the name finder this way:
                </p><pre class="screen">
                
$ opennlp TokenNameFinderTrainer -model en_ner_person.bin -iterations 500 \
                                 -lang en -data corpus_train.txt -encoding utf8
		        </pre><p>
            </p>
            <p>
                Either way you should see the following output during the training process:
		        </p><pre class="screen">
			    
Indexing events using cutoff of 5

	Computing event counts...  done. 203621 events
	Indexing...  done.
Sorting and merging events... done. Reduced 203621 events to 179409.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 179409
	    Number of Outcomes: 3
	  Number of Predicates: 58814
...done.
Computing model parameters...
Performing 500 iterations.
  1:  .. loglikelihood=-223700.5328318588	0.9453494482396216
  2:  .. loglikelihood=-40525.939777363084	0.9467933071736215
  3:  .. loglikelihood=-24893.98837874921	0.9598518816821447
  4:  .. loglikelihood=-18420.3379471033	0.9712996203731442
... cut lots of iterations ...
498:  .. loglikelihood=-952.8501399442295	0.9988950059178572
499:  .. loglikelihood=-952.0600155746948	0.9988950059178572
500:  .. loglikelihood=-951.2722802086295	0.9988950059178572
Writing name finder model ... done (1.638s)

Wrote name finder model to
path: .\en_ner_person.bin
		        </pre><p>
		    </p>
		</div>
		<div class="section" title="Evaluating with English data"><div class="titlepage"><div><div><h4 class="title"><a name="tools.corpora.conll.2003.evaluation.english"></a>Evaluating with English data</h4></div></div></div>
		
            <p>
                You can evaluate the model for the name finder this way:
                </p><pre class="screen">
                
$ opennlp TokenNameFinderEvaluator.conll03 -model en_ner_person.bin \
                                   -lang en -types per -data eng.testa -encoding utf8
                </pre><p>
            </p>
		    <p>
		        If you converted the test A and B files above, you can use them to evaluate the
                model.
		        </p><pre class="screen">
			
$ opennlp TokenNameFinderEvaluator -model en_ner_person.bin -lang en -data corpus_testa.txt \
                                   -encoding utf8
		        </pre><p>
            </p>
            <p>
                Either way you should see the following output:
		        </p><pre class="screen">
			
Loading Token Name Finder model ... done (0.359s)
current: 190.2 sent/s avg: 190.2 sent/s total: 199 sent
current: 648.3 sent/s avg: 415.9 sent/s total: 850 sent
current: 530.1 sent/s avg: 453.6 sent/s total: 1380 sent
current: 793.8 sent/s avg: 539.0 sent/s total: 2178 sent
current: 705.4 sent/s avg: 571.9 sent/s total: 2882 sent


Average: 569.4 sent/s
Total: 3251 sent
Runtime: 5.71s

Precision: 0.9366247297154147
Recall: 0.739956568946797
F-Measure: 0.8267557582133971
		</pre><p>
		</p>
		</div>
	</div>
	</div>
	<div class="section" title="Arvores Deitadas"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.corpora.arvores-deitadas"></a>Arvores Deitadas</h2></div></div></div>
		
		<p>
		The Portuguese corpora available at <a class="ulink" href="http://www.linguateca.pt" target="_top">Floresta Sint&aacute;(c)tica</a> project follow the Arvores Deitadas (AD) format. Apache OpenNLP includes tools to convert from AD format to native format.  
		</p>		
		<div class="section" title="Getting the data"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.arvores-deitadas.getting"></a>Getting the data</h3></div></div></div>
			
			<p>
			The Corpus can be downloaded from here: <a class="ulink" href="http://www.linguateca.pt/floresta/corpus.html" target="_top">http://www.linguateca.pt/floresta/corpus.html</a>
			</p>
			<p>
			The Name Finder models were trained using the Amazonia corpus: <a class="ulink" href="http://www.linguateca.pt/floresta/ficheiros/gz/amazonia.ad.gz" target="_top">amazonia.ad</a>.
			The Chunker models were trained using the <a class="ulink" href="http://www.linguateca.pt/floresta/ficheiros/gz/Bosque_CF_8.0.ad.txt.gz" target="_top">Bosque_CF_8.0.ad</a>.
			</p>
		</div>
		
		<div class="section" title="Converting the data (optional)"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.arvores-deitadas.converting"></a>Converting the data (optional)</h3></div></div></div>
			
			    <p>
				    To extract NameFinder training data from Amazonia corpus:
			        </p><pre class="screen">
			        
$ opennlp TokenNameFinderConverter ad -lang pt -encoding ISO-8859-1 -data amazonia.ad &gt; corpus.txt
			        </pre><p>
			</p>
			<p>
				To extract Chunker training data from Bosque_CF_8.0.ad corpus:
			    </p><pre class="screen">
			    
$ opennlp ChunkerConverter ad -lang pt -data Bosque_CF_8.0.ad.txt -encoding ISO-8859-1 &gt; bosque-chunk
    			</pre><p>
			</p>
		</div>
		<div class="section" title="Training and Evaluation"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.arvores-deitadas.evaluation"></a>Training and Evaluation</h3></div></div></div>
			
			    <p>
			        To perform the evaluation the corpus was split into a training and a test part.
			        </p><pre class="screen">
			        
$ sed '1,55172d' corpus.txt &gt; corpus_train.txt
$ sed '55172,100000000d' corpus.txt &gt; corpus_test.txt
        			</pre><p>
        			</p><pre class="screen">
        			
$ opennlp TokenNameFinderTrainer -model pt-ner.bin -cutoff 20 -lang PT -data corpus_train.txt -encoding UTF-8
...
$ opennlp TokenNameFinderEvaluator -model pt-ner.bin -lang PT -data corpus_train.txt -encoding UTF-8

Precision: 0.8005071889818507
Recall: 0.7450581122145297
F-Measure: 0.7717879983140168
			</pre><p>
			</p>
		</div>
	</div>
	<div class="section" title="Leipzig Corpora"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.corpora.leipzig"></a>Leipzig Corpora</h2></div></div></div>
	
	<p>
	The Leipzig Corpora collection presents corpora in different languages. The corpora is a collection of individual sentences collected
	from the web and newspapers. The Corpora is available as plain text and as MySQL database tables. The OpenNLP integration can only
	use the plain text version.
	</p>
	<p>
	The corpora in the different languages can be used to train a document categorizer model which can detect the document language. 
	The	individual plain text packages can be downloaded here:
	<a class="ulink" href="http://corpora.uni-leipzig.de/download.html" target="_top">http://corpora.uni-leipzig.de/download.html</a>
	</p>
	
	<p>
	After all packages have been downloaded, unzip them and use the following commands to
	produce a training file which can be processed by the Document Categorizer:
	</p><pre class="screen">
			
$ opennlp DoccatConverter leipzig -lang cat -data Leipzig/cat100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang de -data Leipzig/de100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang dk -data Leipzig/dk100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang ee -data Leipzig/ee100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang en -data Leipzig/en100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang fi -data Leipzig/fi100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang fr -data Leipzig/fr100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang it -data Leipzig/it100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang jp -data Leipzig/jp100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang kr -data Leipzig/kr100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang nl -data Leipzig/nl100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang no -data Leipzig/no100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang se -data Leipzig/se100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang sorb -data Leipzig/sorb100k/sentences.txt &gt;&gt; lang.train
$ opennlp DoccatConverter leipzig -lang tr -data Leipzig/tr100k/sentences.txt &gt;&gt; lang.train
	</pre><p>
	</p>
	<p>
	Depending on your platform local it might be problematic to output characters which are not supported by that encoding,
	we suggest to run these command on a platform which has a unicode default encoding, e.g. Linux with UTF-8.
	</p>
	<p>
	After the lang.train file is created the actual language detection document categorizer model
	can be created with the following command.
	</p><pre class="screen">
			
$ opennlp DoccatTrainer -model lang.model -lang x-unspecified -data lang.train -encoding MacRoman

Indexing events using cutoff of 5

	Computing event counts...  done. 10000 events
	Indexing...  done.
Sorting and merging events... done. Reduced 10000 events to 10000.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 10000
	    Number of Outcomes: 2
	  Number of Predicates: 42730
...done.
Computing model parameters...
Performing 100 iterations.
  1:  .. loglikelihood=-6931.471805600547	0.5
  2:  .. loglikelihood=-2110.9654348555955	1.0
... cut lots of iterations ...

 99:  .. loglikelihood=-0.449640418555347	1.0
100:  .. loglikelihood=-0.443746359746235	1.0
Writing document categorizer model ... done (1.210s)

Wrote document categorizer model to
path: /Users/joern/dev/opennlp-apache/opennlp/opennlp-tools/lang.model

	</pre><p>
	In the sample above the language detection model was trained to distinguish two languages, danish and english.
	</p>
	
	<p>
	After the model is created it can be used to detect the two languages:
	
	</p><pre class="programlisting">
			
$ bin/opennlp Doccat ../lang.
lang.model  lang.train  
karkand:opennlp-tools joern$ bin/opennlp Doccat ../lang.model
Loading Document Categorizer model ... done (0.289s)
The American Finance Association is pleased to announce the award of ...
en	The American Finance Association is pleased to announce the award of ..
.
Danskerne skal betale for den &oslash;konomiske krise ved at blive l&aelig;ngere p&aring; arbejdsmarkedet .
dk	Danskerne skal betale for den &oslash;konomiske krise ved at blive l&aelig;ngere p&aring; arbejdsmarkedet .	
	</pre><p>
	</p>
	</div>
		<div class="section" title="OntoNotes Release 4.0"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.corpora.ontonotes"></a>OntoNotes Release 4.0</h2></div></div></div>
		
	<p>
		"OntoNotes Release 4.0, Linguistic Data Consortium (LDC) catalog number
		LDC2011T03 and isbn 1-58563-574-X, was developed as part of the
		OntoNotes project, a collaborative effort between BBN Technologies,
		the University of Colorado, the University of Pennsylvania and the
		University of Southern Californias Information Sciences Institute. The
		goal of the project is to annotate a large corpus comprising various
		genres of text (news, conversational telephone speech, weblogs, usenet
		newsgroups, broadcast, talk shows) in three languages (English,
		Chinese, and Arabic) with structural information (syntax and predicate
		argument structure) and shallow semantics (word sense linked to an
		ontology and coreference). OntoNotes Release 4.0 is supported by the
		Defense Advance Research Project Agency, GALE Program Contract No.
		HR0011-06-C-0022.
	</p>
	<p>
		OntoNotes Release 4.0 contains the content of earlier releases -- OntoNotes
		Release 1.0 LDC2007T21, OntoNotes Release 2.0 LDC2008T04 and OntoNotes
		Release 3.0 LDC2009T24 -- and adds newswire, broadcast news, broadcast
		conversation and web data in English and Chinese and newswire data in
		Arabic. This cumulative publication consists of 2.4 million words as
		follows: 300k words of Arabic newswire 250k words of Chinese newswire,
		250k words of Chinese broadcast news, 150k words of Chinese broadcast
		conversation and 150k words of Chinese web text and 600k words of
		English newswire, 200k word of English broadcast news, 200k words of
		English broadcast conversation and 300k words of English web text.
	</p>
	<p>
		The OntoNotes project builds on two time-tested resources, following the
		Penn Treebank for syntax and the Penn PropBank for predicate-argument
		structure. Its semantic representation will include word sense
		disambiguation for nouns and verbs, with each word sense connected to
		an ontology, and coreference. The current goals call for annotation of
		over a million words each of English and Chinese, and half a million
		words of Arabic over five years." (http://catalog.ldc.upenn.edu/LDC2011T03)
	</p>
		<div class="section" title="Name Finder Training"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.ontonotes.namefinder"></a>Name Finder Training</h3></div></div></div>
		
	<p>
		The OntoNotes corpus can be used to train the Name Finder. The corpus
		contains many different name types
		to train a model for a specific type only the built-in type filter
		option should be used.
	</p>
		<p>
		The sample shows how to train a model to detect person names.		
			</p><pre class="programlisting">
			
$ bin/opennlp TokenNameFinderTrainer.ontonotes -lang en -model en-ontonotes.bin \
			 -nameTypes person -ontoNotesDir ontonotes-release-4.0/data/files/data/english/
			 
Indexing events using cutoff of 5

	Computing event counts...  done. 1953446 events
	Indexing...  done.
Sorting and merging events... done. Reduced 1953446 events to 1822037.
Done indexing.
Incorporating indexed data for training...  
done.
	Number of Event Tokens: 1822037
	    Number of Outcomes: 3
	  Number of Predicates: 298263
...done.
Computing model parameters ...
Performing 100 iterations.
  1:  ... loglikelihood=-2146079.7808976253	0.976677625078963
  2:  ... loglikelihood=-195016.59754190338	0.976677625078963
... cut lots of iterations ...			 
 99:  ... loglikelihood=-10269.902459614596	0.9987299367374374
100:  ... loglikelihood=-10227.160010853702	0.9987314724850341
Writing name finder model ... done (2.315s)

Wrote name finder model to
path: /dev/opennlp/trunk/opennlp-tools/en-ontonotes.bin	
	</pre><p>
		</p>
		</div>
	</div>

	<div class="section" title="Brat Format Support"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.corpora.brat"></a>Brat Format Support</h2></div></div></div>
		
		<p>
			The brat annotation tool is an online environment for collaborative text annotation and
			supports labeling documents with named entities. The best performance of a name finder
			can only be achieved if it was trained on documents similar to the the documents it will
			process. For that reason it is often necessary to manually label a large number of documents and
			build a custom corpus. This is where brat comes in handy.

			</p><table border="0" summary="manufactured viewport for HTML img" cellspacing="0" cellpadding="0" width="585"><tr style="height: 360px"><td><img src="images/brat.png" height="360"></td></tr></table><p>

			OpenNLP can directly be trained and evaluated on labeled data in the brat format.
			Instructions on how to use, download and install brat can be found on the project website:

			<a class="ulink" href="http://brat.nlplab.org" target="_top">http://brat.nlplab.org</a>

			Configuration of brat, including setting up the different entities and relations can be found at:

			<a class="ulink" href="http://brat.nlplab.org/configuration.html" target="_top">http://brat.nlplab.org/configuration.html</a>

		</p>


		<div class="section" title="Sentences and Tokens"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.brat.webtool"></a>Sentences and Tokens</h3></div></div></div>
			
			<p>
				The brat annotation tool only adds named entity spans to the data and doesn't provide information
				about tokens and sentences. To train the name finder this information is required. By default it
				is assumed that each line is a sentence and that tokens are whitespace separated. This can be
				adjusted by providing a custom sentence detector and optional also a tokenizer.

				The opennlp brat command supports the following arguments for providing custom sentence detector
				and tokenizer.

				</p><table border="0" summary="Simple list" class="simplelist"><tr><td><p>-sentenceDetectorModel - your sentence model</p></td></tr><tr><td><p>-tokenizerModel - your tokenizer model</p></td></tr><tr><td><p>-ruleBasedTokenizer - simple | whitespace</p></td></tr></table><p>

			</p>
		</div>

		<div class="section" title="Training"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.brat.training"></a>Training</h3></div></div></div>
			
			<p>
				To train your namefinder model using your brat annotated files you can either use the opennlp command
				line tool or call opennlp.tools.cmdline.CLI main class from your preferred IDE.

				Calling opennlp TokenNameFinder.brat without arguments gives you a list of all the arguments you can use.
				Obviously some combinations are not valid. E.g. you should not provide a token model and also define
				a rule based tokenizer.

				</p><pre class="screen">
					
$ opennlp TokenNameFinderTrainer.brat
Usage: opennlp TokenNameFinderTrainer.brat [-factory factoryName] [-resources resourcesDir] [-type modelType]
[-featuregen featuregenFile] [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language
-model modelFile [-tokenizerModel modelFile] [-ruleBasedTokenizer name] -annotationConfig annConfFile
-bratDataDir bratDataDir [-recursive value] [-sentenceDetectorModel modelFile]

Arguments description:
	-factory factoryName
		A sub-class of TokenNameFinderFactory
	-resources resourcesDir
		The resources directory
	-type modelType
		The type of the token name finder model
	-featuregen featuregenFile
		The feature generator descriptor file
	-nameTypes types
		name types to use for training
	-sequenceCodec codec
		sequence codec used to code name spans
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-tokenizerModel modelFile
	-ruleBasedTokenizer name
	-annotationConfig annConfFile
	-bratDataDir bratDataDir
		location of brat data dir
	-recursive value
	-sentenceDetectorModel modelFile

				</pre><p>

				The following command will train a danish organization name finder model.

				</p><pre class="screen">
					
$ opennlp TokenNameFinderTrainer.brat -resources conf/resources \
-featuregen conf/resources/fg-da-org.xml -nameTypes Organization \
-params conf/resources/TrainerParams.txt -lang da \
-model models/da-org.bin -ruleBasedTokenizer simple \
-annotationConfig data/annotation.conf -bratDataDir data/gold/da/train \
-recursive true -sentenceDetectorModel models/da-sent.bin

Indexing events using cutoff of 0

Computing event counts...
done. 620738 events
Indexing...  done.
Collecting events... Done indexing.
Incorporating indexed data for training...
done.
	Number of Event Tokens: 620738
	    Number of Outcomes: 3
	  Number of Predicates: 1403655
Computing model parameters...
Performing 100 iterations.
  1:  . (614536/620738) 0.9900086671027067
  2:  . (617590/620738) 0.9949286172265915
  3:  . (618615/620738) 0.9965798775006525
  4:  . (619263/620738) 0.9976237961909856
  5:  . (619509/620738) 0.9980200986567602
  6:  . (619830/620738) 0.9985372250450271
  7:  . (619968/620738) 0.9987595410624128
  8:  . (620110/620738) 0.9989883010223315
  9:  . (620200/620738) 0.9991332897293222
 10:  . (620266/620738) 0.9992396147811153
 20:  . (620538/620738) 0.999677802873354
 30:  . (620641/620738) 0.9998437343935767
 40:  . (620653/620738) 0.9998630662211755
Stopping: change in training set accuracy less than 1.0E-5
Stats: (620594/620738) 0.9997680180688149
...done.

Writing name finder model ... Training data summary:
#Sentences: 26133
#Tokens: 620738
#Organization entities: 13053

Compressed 1403655 parameters to 116378
4 outcome patterns
done (11.099s)

Wrote name finder model to
path: models/da-org.bin

				</pre><p>
			</p>
		</div>

		<div class="section" title="Evaluation"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.brat.evaluation"></a>Evaluation</h3></div></div></div>
			
			<p>
				To evaluate you name finder model opennlp provides an evaluator that works with your brat
				annotated data. Normally you would partition your data in a training set and a test set e.g. 70%
				training and 30% test.
				The training set is of cause only used for training the model and should never be used for
				evaluation. The test set is only used for evaluation. In order to avoid overfitting, it is preferable if the training set and
				test set is somewhat balanced so that both sets represents a broad variety of the entities
				it should be able to identify. Shuffling the data before splitting is most likely sufficient in many cases.

				</p><pre class="screen">
					
$ opennlp TokenNameFinderEvaluator.brat -model models/da-org.bin \
-ruleBasedTokenizer simple -annotationConfig data/annotation.conf \
-bratDataDir data/gold/da/test -recursive true \
-sentenceDetectorModel models/da-sent.bin

Loading Token Name Finder model ... done (12.395s)

Average: 610.7 sent/s
Total: 6133 sent
Runtime: 10.043s

Precision: 0.7321974661424203
Recall: 0.25176505933603727
F-Measure: 0.3746926000447127


				</pre><p>
			</p>
		</div>

		<div class="section" title="Cross Validation"><div class="titlepage"><div><div><h3 class="title"><a name="tools.corpora.brat.cross-validation"></a>Cross Validation</h3></div></div></div>
			
			<p>
				You can also use the cross validation to evaluate you model. This can come in handy when you do
				not have enough data to divide it into a proper training and test set.
				Running cross validation with the misclassified attribute set to true can also be helpful because it
				will identify missed annotations as they will pop up as false positives in the text output.
				</p><pre class="screen">
					
$ opennlp TokenNameFinderCrossValidator.brat -resources conf/resources \
-featuregen conf/resources/fg-da-org.xml -nameTypes Organization \
-params conf/resources/TrainerParams.txt -lang da -misclassified true \
-folds 10 -detailedF true -ruleBasedTokenizer simple -annotationConfig data/annotation.conf \
-bratDataDir data/gold/da -recursive true -sentenceDetectorModel models/da-sent.bin

Indexing events using cutoff of 0

Computing event counts...
done. 555858 events
    Indexing...  done.
Collecting events... Done indexing.
Incorporating indexed data for training...
done.
	Number of Event Tokens: 555858
	    Number of Outcomes: 3
	  Number of Predicates: 1302740
Computing model parameters...
Performing 100 iterations.
  1:  . (550095/555858) 0.9896322442062541
  2:  . (552971/555858) 0.9948062274897546
...
...
... (training and evaluationg x 10)
...
done

Evaluated 26133 samples with 13053 entities; found: 12174 entities; correct: 10361.
       TOTAL: precision:   85.11%;  recall:   79.38%; F1:   82.14%.
Organization: precision:   85.11%;  recall:   79.38%; F1:   82.14%. [target: 13053; tp: 10361; fp: 1813]


				</pre><p>
			</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;13.&nbsp;Machine Learning"><div class="titlepage"><div><div><h2 class="title"><a name="opennlp.ml"></a>Chapter&nbsp;13.&nbsp;Machine Learning</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#opennlp.ml.maxent">Maximum Entropy</a></span></dt><dd><dl><dt><span class="section"><a href="#opennlp.ml.maxent.impl">Implementation</a></span></dt></dl></dd></dl></div>

	<div class="section" title="Maximum Entropy"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="opennlp.ml.maxent"></a>Maximum Entropy</h2></div></div></div>
		
		<p>
		To explain what maximum entropy is, it will be simplest to quote from Manning and Schutze* (p. 589):
		<span class="quote">&#8220;<span class="quote">
		Maximum entropy modeling is a framework for integrating information from many heterogeneous
		information sources for classification.  The data for a  classification problem is described
		as a (potentially large) number of features.  These features can be quite complex and allow
		the experimenter to make use of prior knowledge about what types of informations are expected
		to be important for classification. Each feature corresponds to a constraint on the model.
		We then compute the maximum entropy model, the model with the maximum entropy of all the models
		that satisfy the constraints.  This term may seem perverse, since we have spent most of the book
		trying to minimize the (cross) entropy of models, but the idea is that we do not want to go beyond
		the data.  If we chose a model with less entropy, we would add `information' constraints to the
		model that are not justified by the empirical evidence available to us. Choosing the maximum
		entropy model is motivated by the desire to preserve as much uncertainty as possible.
		</span>&#8221;</span>
		</p>
		<p>
		So that gives a rough idea of what the maximum entropy framework is.
		Don't assume anything about your probability distribution other than what you have observed. 
		</p>
		<p>
		On the engineering level, using maxent is an excellent way of creating programs which perform
		very difficult classification tasks very well.  For example,  precision and recall figures for
		programs using maxent models have reached (or are) the state of the art on tasks like part of
		speech tagging, sentence detection, prepositional phrase attachment, and named entity recognition.
		On the engineering level, an added benefit is that the person creating a maxent model only needs
		to inform the training procedure of the event space, and need not worry about independence between
		features.
		</p>
		<p>
		While the authors of this implementation of maximum entropy are generally interested using
		maxent models in natural language processing, the framework is certainly quite general and
		useful for a much wider variety of fields.  In fact, maximum entropy modeling was originally
		developed for statistical physics.
		</p>
		<p>
		For a very in-depth discussion of how maxent can be used in natural language processing,
		try reading Adwait Ratnaparkhi's dissertation.   Also,  check out Berger, Della Pietra,
		and Della Pietra's paper A Maximum Entropy Approach to Natural Language Processing, which
		provides an excellent introduction and discussion of the framework.
		</p>
		<p>
		*Foundations of statistical natural language processing . Christopher D. Manning, Hinrich Schutze. 
		Cambridge, Mass. : MIT Press, c1999.
		</p>
			<div class="section" title="Implementation"><div class="titlepage"><div><div><h3 class="title"><a name="opennlp.ml.maxent.impl"></a>Implementation</h3></div></div></div>
		
		<p>
		We have tried to make the opennlp.maxent implementation easy to use.  To create a model, one
		needs (of course) the training data, and then implementations of two interfaces in the
		opennlp.maxent package, EventStream and ContextGenerator.  These have fairly simple specifications,
		and example implementations can be found in the OpenNLP Tools preprocessing components.
		</p>
		<p>
		We have also set in place some interfaces and code to make it easier to automate the training
		and evaluation process (the Evalable interface and the TrainEval class).  It is not necessary
		to use this functionality, but if you do you'll find it much easier to see how well your models
		are doing.  The opennlp.grok.preprocess.namefind package is an example of a maximum entropy
		component which uses this functionality.
		</p>
		<p>
		We have managed to use several techniques to reduce the size of the models when writing them to
		disk, which also means that reading in a model for use is much quicker than with less compact
		encodings of the model.  This was especially important to us since we use many maxent models in
		the Grok library, and we wanted the start up time and the physical size of the library to be as
		minimal as possible. As of version 1.2.0, maxent has an io package which greatly simplifies the
		process of loading and saving models in different formats.
		</p>
		</div>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;14.&nbsp;UIMA Integration"><div class="titlepage"><div><div><h2 class="title"><a name="org.apche.opennlp.uima"></a>Chapter&nbsp;14.&nbsp;UIMA Integration</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#org.apche.opennlp.running-pear-sample">Running the pear sample in CVD</a></span></dt><dt><span class="section"><a href="#org.apche.opennlp.further-help">Further Help</a></span></dt></dl></div>

<p>
	The UIMA Integration wraps the OpenNLP components in UIMA Analysis Engines which can 
	be used to automatically annotate text and train new OpenNLP models from annotated text.
</p>
	<div class="section" title="Running the pear sample in CVD"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="org.apche.opennlp.running-pear-sample"></a>Running the pear sample in CVD</h2></div></div></div>
		
		<p>
			The Cas Visual Debugger is shipped as part of the UIMA distribution and is a tool which can run
			the OpenNLP UIMA Annotators and display their analysis results. The source distribution comes with a script
			which can create a sample UIMA application. Which includes the sentence detector, tokenizer,
			pos tagger, chunker and name finders for English. This sample application is packaged in the
			pear format and must be installed with the pear installer before it can be run by CVD.
			Please consult the UIMA documentation for further information about the pear installer.
		</p>
		<p>
			The OpenNLP UIMA pear file must be build manually.
			First download the source distribution, unzip it and go to the apache-opennlp/opennlp folder.
			Type "mvn install" to build everything. Now build the pear file, go to apache-opennlp/opennlp-uima
			and build it as shown below. Note the models will be downloaded
			from the old SourceForge repository and are not licensed under the AL 2.0.
			</p><pre class="screen">
			
$ ant -f createPear.xml 
Buildfile: createPear.xml

createPear:
     [echo] ##### Creating OpenNlpTextAnalyzer pear #####
     [copy] Copying 13 files to OpenNlpTextAnalyzer/desc
     [copy] Copying 1 file to OpenNlpTextAnalyzer/metadata
     [copy] Copying 1 file to OpenNlpTextAnalyzer/lib
     [copy] Copying 3 files to OpenNlpTextAnalyzer/lib
    [mkdir] Created dir: OpenNlpTextAnalyzer/models
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-token.bin
      [get] To: OpenNlpTextAnalyzer/models/en-token.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-sent.bin
      [get] To: OpenNlpTextAnalyzer/models/en-sent.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-date.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-date.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-location.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-location.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-money.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-money.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-organization.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-organization.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-percentage.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-percentage.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-person.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-person.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-ner-time.bin
      [get] To: OpenNlpTextAnalyzer/models/en-ner-time.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-pos-maxent.bin
      [get] To: OpenNlpTextAnalyzer/models/en-pos-maxent.bin
      [get] Getting: http://opennlp.sourceforge.net/models-1.5/en-chunker.bin
      [get] To: OpenNlpTextAnalyzer/models/en-chunker.bin
      [zip] Building zip: OpenNlpTextAnalyzer.pear

BUILD SUCCESSFUL
Total time: 3 minutes 20 seconds
		 </pre><p>
		</p>
		<p>
			After the pear is installed start the Cas Visual Debugger shipped with the UIMA framework.
			And click on Tools -&gt; Load AE. Then select the opennlp.uima.OpenNlpTextAnalyzer_pear.xml
			file in the file dialog. Now enter some text and start the analysis engine with
			"Run -&gt; Run OpenNLPTextAnalyzer". Afterwards the results will be displayed.
			You should see sentences, tokens, chunks, pos tags and maybe some names. Remember the input text
			must be written in English.
		</p>
	</div>
	<div class="section" title="Further Help"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="org.apche.opennlp.further-help"></a>Further Help</h2></div></div></div>
		
		<p>
			For more information about how to use the integration please consult the javadoc of the individual
			Analysis Engines and checkout the included xml descriptors.
		</p>
		<p>
			TODO: Extend this documentation with information about the individual components.
			If you want to contribute please contact us on the mailing list
			or comment on the jira issue <a class="ulink" href="https://issues.apache.org/jira/browse/OPENNLP-49" target="_top">OPENNLP-49</a>.
		</p>
	</div>
</div>
	<div class="chapter" title="Chapter&nbsp;15.&nbsp;Morfologik Addon"><div class="titlepage"><div><div><h2 class="title"><a name="tools.morfologik-addon"></a>Chapter&nbsp;15.&nbsp;Morfologik Addon</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.morfologik-addon.api">Morfologik Integration</a></span></dt><dt><span class="section"><a href="#tools.morfologik-addon.cmdline">Morfologik CLI Tools</a></span></dt></dl></div>
	
		<p>
			<a class="ulink" href="https://github.com/morfologik/morfologik-stemming" target="_top"><em class="citetitle">Morfologik</em></a>
			provides tools for finite state automata (FSA) construction and dictionary-based morphological dictionaries.
		</p>
		<p>
			The Morfologik Addon implements OpenNLP interfaces and extensions to allow the use of FSA Morfologik dictionary tools.
		</p>
		<div class="section" title="Morfologik Integration"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.morfologik-addon.api"></a>Morfologik Integration</h2></div></div></div>
			
			<p>
			To allow for an easy integration with OpenNLP, the following implementations are provided:
			</p><div class="itemizedlist"><ul class="itemizedlist" type="opencircle"><li class="listitem" style="list-style-type: circle">
					<p>
					The <code class="code">MorfologikPOSTaggerFactory</code> extends <code class="code">POSTaggerFactory</code>, which helps creating a POSTagger model with an embedded FSA TagDictionary.
					</p>
				</li><li class="listitem" style="list-style-type: circle">
					<p>
					The <code class="code">MorfologikTagDictionary</code> implements a FSA based <code class="code">TagDictionary</code>, allowing for much smaller files than the default XML based with improved memory consumption.
					</p>
				</li><li class="listitem" style="list-style-type: circle">
					<p>
					The <code class="code">MorfologikLemmatizer</code> implements a FSA based <code class="code">Lemmatizer</code> dictionaries.
					</p>
				</li></ul></div><p>
		</p>
		<p>
		The first two implementations can be used directly from command line, as in the example bellow. Having a FSA Morfologik dictionary (see next section how to build one), you can train a POS Tagger
		model with an embedded FSA dictionary. 
		</p>
		<p>
		The example trains a POSTagger with a CONLL corpus named <code class="code">portuguese_bosque_train.conll</code> and a FSA dictionary named 
		<code class="code">pt-morfologik.dict</code>. It will output a model named <code class="code">pos-pt_fsadic.model</code>.
		
		</p><pre class="screen">
		
$ bin/opennlp POSTaggerTrainer -type perceptron -lang pt -model pos-pt_fsadic.model -data portuguese_bosque_train.conll \
	 -encoding UTF-8 -factory opennlp.morfologik.tagdict.MorfologikPOSTaggerFactory -dict pt-morfologik.dict
		</pre><p>
		
		</p>
		<p>
		Another example follows. It shows how to use the <code class="code">MorfologikLemmatizer</code>. You will need a lemma dictionary and info file, in this example, we will use a very small Portuguese dictionary. 
		Its syntax is <code class="code">lemma,lexeme,postag</code>.
		</p>
		<p>
		File <code class="code">lemmaDictionary.txt:</code>
		</p><pre class="screen">
		
casa,casa,NOUN
casar,casa,V
casar,casar,V-INF
Casa,Casa,PROP
casa,casinha,NOUN
casa,casona,NOUN
menino,menina,NOUN
menino,menino,NOUN
menino,menin&atilde;o,NOUN
menino,menininho,NOUN
carro,carro,NOUN
		</pre><p>
		</p>
		<p>
		Mandatory metadata file, which must have the same name but .info extension <code class="code">lemmaDictionary.info:</code>
		</p><pre class="screen">
		
#
# REQUIRED PROPERTIES
#

# Column (lemma, inflected, tag) separator. This must be a single byte in the target encoding.
fsa.dict.separator=,

# The charset in which the input is encoded. UTF-8 is strongly recommended.
fsa.dict.encoding=UTF-8

# The type of lemma-inflected form encoding compression that precedes automaton
# construction. Allowed values: [suffix, infix, prefix, none].
# Details are in Daciuk's paper and in the code. 
# Leave at 'prefix' if not sure.
fsa.dict.encoder=prefix
		
		</pre><p>
		</p>
		<p>
		The following code creates a binary FSA Morfologik dictionary, loads it in MorfologikLemmatizer and uses it to 
		find the lemma the word "casa" noun and verb.
		
				</p><pre class="programlisting">
		
<i class="hl-comment" style="color: silver">// Part 1: compile a FSA lemma dictionary </i>
   
<i class="hl-comment" style="color: silver">// we need the tabular dictionary. It is mandatory to have info </i>
<i class="hl-comment" style="color: silver">//  file with same name, but .info extension</i>
Path textLemmaDictionary = Paths.get(<b class="hl-string"><i style="color:red">"dictionaryWithLemma.txt"</i></b>);

<i class="hl-comment" style="color: silver">// this will build a binary dictionary located in compiledLemmaDictionary</i>
Path compiledLemmaDictionary = <b class="hl-keyword">new</b> MorfologikDictionayBuilder()
    .build(textLemmaDictionary);

<i class="hl-comment" style="color: silver">// Part 2: load a MorfologikLemmatizer and use it</i>
MorfologikLemmatizer lemmatizer = <b class="hl-keyword">new</b> MorfologikLemmatizer(compiledLemmaDictionary);

String[] toks = {<b class="hl-string"><i style="color:red">"casa"</i></b>, <b class="hl-string"><i style="color:red">"casa"</i></b>};
String[] tags = {<b class="hl-string"><i style="color:red">"NOUN"</i></b>, <b class="hl-string"><i style="color:red">"V"</i></b>};

String[] lemmas = lemmatizer.lemmatize(toks, tags);
System.out.println(Arrays.toString(lemmas)); <i class="hl-comment" style="color: silver">// outputs [casa, casar]</i>
    
			</pre><p>
		
		</p>
		</div>
		<div class="section" title="Morfologik CLI Tools"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.morfologik-addon.cmdline"></a>Morfologik CLI Tools</h2></div></div></div>
			
			<p>
				The Morfologik addon provides a command line tool. <code class="code">XMLDictionaryToTable</code> makes easy to convert from an OpenNLP XML based dictionary
				to a tabular format. <code class="code">MorfologikDictionaryBuilder</code> can take a tabular dictionary and output a binary Morfologik FSA dictionary.
			</p>
			<pre class="screen">
		
$ sh bin/morfologik-addon
OpenNLP Morfologik Addon. Usage: opennlp-morfologik-addon TOOL
where TOOL is one of:
  MorfologikDictionaryBuilder    builds a binary POS Dictionary using Morfologik
  XMLDictionaryToTable           reads an OpenNLP XML tag dictionary and outputs it in a tabular file
All tools print help when invoked with help parameter
Example: opennlp-morfologik-addon POSDictionaryBuilder help
		
		</pre>
		</div>
</div>
	<div class="chapter" title="Chapter&nbsp;16.&nbsp;The Command Line Interface"><div class="titlepage"><div><div><h2 class="title"><a name="tools.cli"></a>Chapter&nbsp;16.&nbsp;The Command Line Interface</h2></div></div></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="section"><a href="#tools.cli.doccat">Doccat</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.doccat.Doccat">Doccat</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatTrainer">DoccatTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatEvaluator">DoccatEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatCrossValidator">DoccatCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.doccat.DoccatConverter">DoccatConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.dictionary">Dictionary</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.dictionary.DictionaryBuilder">DictionaryBuilder</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.tokenizer">Tokenizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.tokenizer.SimpleTokenizer">SimpleTokenizer</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerME">TokenizerME</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerTrainer">TokenizerTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerMEEvaluator">TokenizerMEEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerCrossValidator">TokenizerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.TokenizerConverter">TokenizerConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.tokenizer.DictionaryDetokenizer">DictionaryDetokenizer</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.sentdetect">Sentdetect</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetector">SentenceDetector</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorTrainer">SentenceDetectorTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorEvaluator">SentenceDetectorEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorCrossValidator">SentenceDetectorCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.sentdetect.SentenceDetectorConverter">SentenceDetectorConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.namefind">Namefind</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinder">TokenNameFinder</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderTrainer">TokenNameFinderTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderEvaluator">TokenNameFinderEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderCrossValidator">TokenNameFinderCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.TokenNameFinderConverter">TokenNameFinderConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.namefind.CensusDictionaryCreator">CensusDictionaryCreator</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.postag">Postag</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.postag.POSTagger">POSTagger</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerTrainer">POSTaggerTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerEvaluator">POSTaggerEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerCrossValidator">POSTaggerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.postag.POSTaggerConverter">POSTaggerConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.lemmatizer">Lemmatizer</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerME">LemmatizerME</a></span></dt><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerTrainerME">LemmatizerTrainerME</a></span></dt><dt><span class="section"><a href="#tools.cli.lemmatizer.LemmatizerEvaluator">LemmatizerEvaluator</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.chunker">Chunker</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.chunker.ChunkerME">ChunkerME</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerTrainerME">ChunkerTrainerME</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerEvaluator">ChunkerEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerCrossValidator">ChunkerCrossValidator</a></span></dt><dt><span class="section"><a href="#tools.cli.chunker.ChunkerConverter">ChunkerConverter</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.parser">Parser</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.parser.Parser">Parser</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserTrainer">ParserTrainer</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserEvaluator">ParserEvaluator</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.ParserConverter">ParserConverter</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.BuildModelUpdater">BuildModelUpdater</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.CheckModelUpdater">CheckModelUpdater</a></span></dt><dt><span class="section"><a href="#tools.cli.parser.TaggerModelReplacer">TaggerModelReplacer</a></span></dt></dl></dd><dt><span class="section"><a href="#tools.cli.entitylinker">Entitylinker</a></span></dt><dd><dl><dt><span class="section"><a href="#tools.cli.entitylinker.EntityLinker">EntityLinker</a></span></dt></dl></dd></dl></div>


<p>This section details the available tools and parameters of the Command Line Interface. For a introduction in its usage please refer to <a class="xref" href="#intro.cli" title="Command line interface (CLI)">the section called &#8220;Command line interface (CLI)&#8221;</a>.  </p>

<div class="section" title="Doccat"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.doccat"></a>Doccat</h2></div></div></div>


<div class="section" title="Doccat"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.doccat.Doccat"></a>Doccat</h3></div></div></div>


<p>Learnable document categorizer</p>

<pre class="screen">

Usage: opennlp Doccat model &lt; documents


</pre> 
</div>

<div class="section" title="DoccatTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.doccat.DoccatTrainer"></a>DoccatTrainer</h3></div></div></div>


<p>Trainer for the learnable document categorizer</p>

<pre class="screen">

Usage: opennlp DoccatTrainer[.leipzig] [-factory factoryName] [-tokenizer tokenizer] [-featureGenerators fg] 
        [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of DoccatFactory where to get implementation and resources.
	-tokenizer tokenizer
		Tokenizer implementation. WhitespaceTokenizer is used if not specified.
	-featureGenerators fg
		Comma separated feature generator classes. Bag of words is used if not specified.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="2" align="left" valign="middle">leipzig</td><td align="left">sentencesDir</td><td align="left">sentencesDir</td><td align="left">No</td><td align="left">Dir with Leipig sentences to be used</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="DoccatEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.doccat.DoccatEvaluator"></a>DoccatEvaluator</h3></div></div></div>


<p>Measures the performance of the Doccat model with the reference data</p>

<pre class="screen">

Usage: opennlp DoccatEvaluator[.leipzig] [-reportOutputFile outputFile] [-misclassified true|false] -model 
        model -data sampleData [-encoding charsetName] 
Arguments description:
	-reportOutputFile outputFile
		the path of the fine-grained report file.
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="2" align="left" valign="middle">leipzig</td><td align="left">sentencesDir</td><td align="left">sentencesDir</td><td align="left">No</td><td align="left">Dir with Leipig sentences to be used</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="DoccatCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.doccat.DoccatCrossValidator"></a>DoccatCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the learnable Document Categorizer</p>

<pre class="screen">

Usage: opennlp DoccatCrossValidator[.leipzig] [-reportOutputFile outputFile] [-misclassified true|false] 
        [-folds num] [-factory factoryName] [-tokenizer tokenizer] [-featureGenerators fg] [-params 
        paramsFile] -lang language -data sampleData [-encoding charsetName] 
Arguments description:
	-reportOutputFile outputFile
		the path of the fine-grained report file.
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-factory factoryName
		A sub-class of DoccatFactory where to get implementation and resources.
	-tokenizer tokenizer
		Tokenizer implementation. WhitespaceTokenizer is used if not specified.
	-featureGenerators fg
		Comma separated feature generator classes. Bag of words is used if not specified.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="2" align="left" valign="middle">leipzig</td><td align="left">sentencesDir</td><td align="left">sentencesDir</td><td align="left">No</td><td align="left">Dir with Leipig sentences to be used</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="DoccatConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.doccat.DoccatConverter"></a>DoccatConverter</h3></div></div></div>


<p>Converts leipzig data format to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp DoccatConverter help|leipzig [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="2" align="left" valign="middle">leipzig</td><td align="left">sentencesDir</td><td align="left">sentencesDir</td><td align="left">No</td><td align="left">Dir with Leipig sentences to be used</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

</div>

<div class="section" title="Dictionary"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.dictionary"></a>Dictionary</h2></div></div></div>


<div class="section" title="DictionaryBuilder"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.dictionary.DictionaryBuilder"></a>DictionaryBuilder</h3></div></div></div>


<p>Builds a new dictionary</p>

<pre class="screen">

Usage: opennlp DictionaryBuilder -outputFile out -inputFile in [-encoding charsetName]

Arguments description:
	-outputFile out
		The dictionary file.
	-inputFile in
		Plain file with one entry per line
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
</div>

</div>

<div class="section" title="Tokenizer"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.tokenizer"></a>Tokenizer</h2></div></div></div>


<div class="section" title="SimpleTokenizer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.SimpleTokenizer"></a>SimpleTokenizer</h3></div></div></div>


<p>Character class tokenizer</p>

<pre class="screen">

Usage: opennlp SimpleTokenizer &lt; sentences


</pre> 
</div>

<div class="section" title="TokenizerME"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.TokenizerME"></a>TokenizerME</h3></div></div></div>


<p>Learnable tokenizer</p>

<pre class="screen">

Usage: opennlp TokenizerME model &lt; sentences


</pre> 
</div>

<div class="section" title="TokenizerTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.TokenizerTrainer"></a>TokenizerTrainer</h3></div></div></div>


<p>Trainer for the learnable tokenizer</p>

<pre class="screen">

Usage: opennlp TokenizerTrainer[.ad|.pos|.conllx|.namefinder|.parse] [-factory factoryName] [-abbDict path] 
        [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] -lang language -model modelFile -data sampleData 
        [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of TokenizerFactory where to get implementation and resources.
	-abbDict path
		abbreviation dictionary in XML format.
	-alphaNumOpt isAlphaNumOpt
		Optimization flag to skip alpha numeric tokens for further tokenization
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenizerMEEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.TokenizerMEEvaluator"></a>TokenizerMEEvaluator</h3></div></div></div>


<p>Evaluator for the learnable tokenizer</p>

<pre class="screen">

Usage: opennlp TokenizerMEEvaluator[.ad|.pos|.conllx|.namefinder|.parse] [-misclassified true|false] -model 
        model -data sampleData [-encoding charsetName] 
Arguments description:
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenizerCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.TokenizerCrossValidator"></a>TokenizerCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the learnable tokenizer</p>

<pre class="screen">

Usage: opennlp TokenizerCrossValidator[.ad|.pos|.conllx|.namefinder|.parse] [-misclassified true|false] 
        [-folds num] [-factory factoryName] [-abbDict path] [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] 
        -lang language -data sampleData [-encoding charsetName] 
Arguments description:
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-factory factoryName
		A sub-class of TokenizerFactory where to get implementation and resources.
	-abbDict path
		abbreviation dictionary in XML format.
	-alphaNumOpt isAlphaNumOpt
		Optimization flag to skip alpha numeric tokens for further tokenization
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenizerConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.TokenizerConverter"></a>TokenizerConverter</h3></div></div></div>


<p>Converts foreign data formats (ad,pos,conllx,namefinder,parse) to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp TokenizerConverter help|ad|pos|conllx|namefinder|parse [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="DictionaryDetokenizer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.tokenizer.DictionaryDetokenizer"></a>DictionaryDetokenizer</h3></div></div></div>


<p></p>

<pre class="screen">

Usage: opennlp DictionaryDetokenizer detokenizerDictionary


</pre> 
</div>

</div>

<div class="section" title="Sentdetect"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.sentdetect"></a>Sentdetect</h2></div></div></div>


<div class="section" title="SentenceDetector"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.sentdetect.SentenceDetector"></a>SentenceDetector</h3></div></div></div>


<p>Learnable sentence detector</p>

<pre class="screen">

Usage: opennlp SentenceDetector model &lt; sentences


</pre> 
</div>

<div class="section" title="SentenceDetectorTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.sentdetect.SentenceDetectorTrainer"></a>SentenceDetectorTrainer</h3></div></div></div>


<p>Trainer for the learnable sentence detector</p>

<pre class="screen">

Usage: opennlp SentenceDetectorTrainer[.ad|.pos|.conllx|.namefinder|.parse] [-factory factoryName] [-eosChars 
        string] [-abbDict path] [-params paramsFile] -lang language -model modelFile -data sampleData 
        [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of SentenceDetectorFactory where to get implementation and resources.
	-eosChars string
		EOS characters.
	-abbDict path
		abbreviation dictionary in XML format.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text.</td></tr><tr><td align="left">includeTitles</td><td align="left">includeTitles</td><td align="left">Yes</td><td align="left">If true will include sentences marked as headlines.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="SentenceDetectorEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.sentdetect.SentenceDetectorEvaluator"></a>SentenceDetectorEvaluator</h3></div></div></div>


<p>Evaluator for the learnable sentence detector</p>

<pre class="screen">

Usage: opennlp SentenceDetectorEvaluator[.ad|.pos|.conllx|.namefinder|.parse] [-misclassified true|false] 
        -model model -data sampleData [-encoding charsetName] 
Arguments description:
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text.</td></tr><tr><td align="left">includeTitles</td><td align="left">includeTitles</td><td align="left">Yes</td><td align="left">If true will include sentences marked as headlines.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="SentenceDetectorCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.sentdetect.SentenceDetectorCrossValidator"></a>SentenceDetectorCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the learnable sentence detector</p>

<pre class="screen">

Usage: opennlp SentenceDetectorCrossValidator[.ad|.pos|.conllx|.namefinder|.parse] [-factory factoryName] 
        [-eosChars string] [-abbDict path] [-params paramsFile] -lang language [-misclassified true|false] 
        [-folds num] -data sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of SentenceDetectorFactory where to get implementation and resources.
	-eosChars string
		EOS characters.
	-abbDict path
		abbreviation dictionary in XML format.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text.</td></tr><tr><td align="left">includeTitles</td><td align="left">includeTitles</td><td align="left">Yes</td><td align="left">If true will include sentences marked as headlines.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

<div class="section" title="SentenceDetectorConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.sentdetect.SentenceDetectorConverter"></a>SentenceDetectorConverter</h3></div></div></div>


<p>Converts foreign data formats (ad,pos,conllx,namefinder,parse) to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp SentenceDetectorConverter help|ad|pos|conllx|namefinder|parse [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text.</td></tr><tr><td align="left">includeTitles</td><td align="left">includeTitles</td><td align="left">Yes</td><td align="left">If true will include sentences marked as headlines.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="3" align="left" valign="middle">pos</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">namefinder</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr><tr><td rowspan="3" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">detokenizer</td><td align="left">dictionary</td><td align="left">No</td><td align="left">Specifies the file with detokenizer dictionary.</td></tr></tbody></table></div>

</div>

</div>

<div class="section" title="Namefind"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.namefind"></a>Namefind</h2></div></div></div>


<div class="section" title="TokenNameFinder"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.TokenNameFinder"></a>TokenNameFinder</h3></div></div></div>


<p>Learnable name finder</p>

<pre class="screen">

Usage: opennlp TokenNameFinder model1 model2 ... modelN &lt; sentences


</pre> 
</div>

<div class="section" title="TokenNameFinderTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.TokenNameFinderTrainer"></a>TokenNameFinderTrainer</h3></div></div></div>


<p>Trainer for the learnable name finder</p>

<pre class="screen">

Usage: opennlp TokenNameFinderTrainer[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile] 
        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language -model modelFile -data 
        sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of TokenNameFinderFactory
	-resources resourcesDir
		The resources directory
	-type modelType
		The type of the token name finder model
	-featuregen featuregenFile
		The feature generator descriptor file
	-nameTypes types
		name types to use for training
	-sequenceCodec codec
		sequence codec used to code name spans
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">evalita</td><td align="left">lang</td><td align="left">it</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,gpe</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll03</td><td align="left">lang</td><td align="left">en|de</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">bionlp2004</td><td align="left">types</td><td align="left">DNA,protein,cell_type,cell_line,RNA</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll02</td><td align="left">lang</td><td align="left">es|nl</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">muc6</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="6" align="left" valign="middle">brat</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">ruleBasedTokenizer</td><td align="left">name</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">annotationConfig</td><td align="left">annConfFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">bratDataDir</td><td align="left">bratDataDir</td><td align="left">No</td><td align="left">Location of brat data dir</td></tr><tr><td align="left">recursive</td><td align="left">value</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">sentenceDetectorModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenNameFinderEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.TokenNameFinderEvaluator"></a>TokenNameFinderEvaluator</h3></div></div></div>


<p>Measures the performance of the NameFinder model with the reference data</p>

<pre class="screen">

Usage: opennlp TokenNameFinderEvaluator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
        [-nameTypes types] [-misclassified true|false] -model model [-detailedF true|false] -data sampleData 
        [-encoding charsetName] 
Arguments description:
	-nameTypes types
		name types to use for evaluation
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-detailedF true|false
		if true will print detailed FMeasure results.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">evalita</td><td align="left">lang</td><td align="left">it</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,gpe</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll03</td><td align="left">lang</td><td align="left">en|de</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">bionlp2004</td><td align="left">types</td><td align="left">DNA,protein,cell_type,cell_line,RNA</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll02</td><td align="left">lang</td><td align="left">es|nl</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">muc6</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="6" align="left" valign="middle">brat</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">ruleBasedTokenizer</td><td align="left">name</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">annotationConfig</td><td align="left">annConfFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">bratDataDir</td><td align="left">bratDataDir</td><td align="left">No</td><td align="left">Location of brat data dir</td></tr><tr><td align="left">recursive</td><td align="left">value</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">sentenceDetectorModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenNameFinderCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.TokenNameFinderCrossValidator"></a>TokenNameFinderCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the learnable Name Finder</p>

<pre class="screen">

Usage: opennlp 
        TokenNameFinderCrossValidator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile] 
        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language [-misclassified 
        true|false] [-folds num] [-detailedF true|false] -data sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of TokenNameFinderFactory
	-resources resourcesDir
		The resources directory
	-type modelType
		The type of the token name finder model
	-featuregen featuregenFile
		The feature generator descriptor file
	-nameTypes types
		name types to use for training
	-sequenceCodec codec
		sequence codec used to code name spans
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-detailedF true|false
		if true will print detailed FMeasure results.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">evalita</td><td align="left">lang</td><td align="left">it</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,gpe</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll03</td><td align="left">lang</td><td align="left">en|de</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">bionlp2004</td><td align="left">types</td><td align="left">DNA,protein,cell_type,cell_line,RNA</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll02</td><td align="left">lang</td><td align="left">es|nl</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">muc6</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="6" align="left" valign="middle">brat</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">ruleBasedTokenizer</td><td align="left">name</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">annotationConfig</td><td align="left">annConfFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">bratDataDir</td><td align="left">bratDataDir</td><td align="left">No</td><td align="left">Location of brat data dir</td></tr><tr><td align="left">recursive</td><td align="left">value</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">sentenceDetectorModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="TokenNameFinderConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.TokenNameFinderConverter"></a>TokenNameFinderConverter</h3></div></div></div>


<p>Converts foreign data formats (evalita,ad,conll03,bionlp2004,conll02,muc6,ontonotes,brat) to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp TokenNameFinderConverter help|evalita|ad|conll03|bionlp2004|conll02|muc6|ontonotes|brat 
        [help|options...] 

</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="4" align="left" valign="middle">evalita</td><td align="left">lang</td><td align="left">it</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,gpe</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">splitHyphenatedTokens</td><td align="left">split</td><td align="left">Yes</td><td align="left">If true all hyphenated tokens will be separated (default true)</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll03</td><td align="left">lang</td><td align="left">en|de</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">bionlp2004</td><td align="left">types</td><td align="left">DNA,protein,cell_type,cell_line,RNA</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="4" align="left" valign="middle">conll02</td><td align="left">lang</td><td align="left">es|nl</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">types</td><td align="left">per,loc,org,misc</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="3" align="left" valign="middle">muc6</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="6" align="left" valign="middle">brat</td><td align="left">tokenizerModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">ruleBasedTokenizer</td><td align="left">name</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">annotationConfig</td><td align="left">annConfFile</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td align="left">bratDataDir</td><td align="left">bratDataDir</td><td align="left">No</td><td align="left">Location of brat data dir</td></tr><tr><td align="left">recursive</td><td align="left">value</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr><tr><td align="left">sentenceDetectorModel</td><td align="left">modelFile</td><td align="left">Yes</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="CensusDictionaryCreator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.namefind.CensusDictionaryCreator"></a>CensusDictionaryCreator</h3></div></div></div>


<p>Converts 1990 US Census names into a dictionary</p>

<pre class="screen">

Usage: opennlp CensusDictionaryCreator [-encoding charsetName] [-lang code] -dict dict -censusData censusDict

Arguments description:
	-encoding charsetName
	-lang code
	-dict dict
	-censusData censusDict


</pre> 
</div>

</div>

<div class="section" title="Postag"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.postag"></a>Postag</h2></div></div></div>


<div class="section" title="POSTagger"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.postag.POSTagger"></a>POSTagger</h3></div></div></div>


<p>Learnable part of speech tagger</p>

<pre class="screen">

Usage: opennlp POSTagger model &lt; sentences


</pre> 
</div>

<div class="section" title="POSTaggerTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.postag.POSTaggerTrainer"></a>POSTaggerTrainer</h3></div></div></div>


<p>Trains a model for the part-of-speech tagger</p>

<pre class="screen">

Usage: opennlp POSTaggerTrainer[.ad|.conllx|.parse|.ontonotes] [-factory factoryName] [-type 
        maxent|perceptron|perceptron_sequence] [-dict dictionaryPath] [-ngram cutoff] [-tagDictCutoff 
        tagDictCutoff] [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding 
        charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of POSTaggerFactory where to get implementation and resources.
	-type maxent|perceptron|perceptron_sequence
		The type of the token name finder model. One of maxent|perceptron|perceptron_sequence.
	-dict dictionaryPath
		The XML tag dictionary file
	-ngram cutoff
		NGram cutoff. If not specified will not create ngram dictionary.
	-tagDictCutoff tagDictCutoff
		TagDictionary cutoff. If specified will create/expand a mutable TagDictionary
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">expandME</td><td align="left">expandME</td><td align="left">Yes</td><td align="left">Expand multiword expressions.</td></tr><tr><td align="left">includeFeatures</td><td align="left">includeFeatures</td><td align="left">Yes</td><td align="left">Combine POS Tags with word features, like number and gender.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="2" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="2" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="POSTaggerEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.postag.POSTaggerEvaluator"></a>POSTaggerEvaluator</h3></div></div></div>


<p>Measures the performance of the POS tagger model with the reference data</p>

<pre class="screen">

Usage: opennlp POSTaggerEvaluator[.ad|.conllx|.parse|.ontonotes] [-reportOutputFile outputFile] 
        [-misclassified true|false] -model model -data sampleData [-encoding charsetName] 
Arguments description:
	-reportOutputFile outputFile
		the path of the fine-grained report file.
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">expandME</td><td align="left">expandME</td><td align="left">Yes</td><td align="left">Expand multiword expressions.</td></tr><tr><td align="left">includeFeatures</td><td align="left">includeFeatures</td><td align="left">Yes</td><td align="left">Combine POS Tags with word features, like number and gender.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="2" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="2" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="POSTaggerCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.postag.POSTaggerCrossValidator"></a>POSTaggerCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the learnable POS tagger</p>

<pre class="screen">

Usage: opennlp POSTaggerCrossValidator[.ad|.conllx|.parse|.ontonotes] [-reportOutputFile outputFile] 
        [-misclassified true|false] [-folds num] [-factory factoryName] [-type 
        maxent|perceptron|perceptron_sequence] [-dict dictionaryPath] [-ngram cutoff] [-tagDictCutoff 
        tagDictCutoff] [-params paramsFile] -lang language -data sampleData [-encoding charsetName] 
Arguments description:
	-reportOutputFile outputFile
		the path of the fine-grained report file.
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-factory factoryName
		A sub-class of POSTaggerFactory where to get implementation and resources.
	-type maxent|perceptron|perceptron_sequence
		The type of the token name finder model. One of maxent|perceptron|perceptron_sequence.
	-dict dictionaryPath
		The XML tag dictionary file
	-ngram cutoff
		NGram cutoff. If not specified will not create ngram dictionary.
	-tagDictCutoff tagDictCutoff
		TagDictionary cutoff. If specified will create/expand a mutable TagDictionary
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">expandME</td><td align="left">expandME</td><td align="left">Yes</td><td align="left">Expand multiword expressions.</td></tr><tr><td align="left">includeFeatures</td><td align="left">includeFeatures</td><td align="left">Yes</td><td align="left">Combine POS Tags with word features, like number and gender.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="2" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="2" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

<div class="section" title="POSTaggerConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.postag.POSTaggerConverter"></a>POSTaggerConverter</h3></div></div></div>


<p>Converts foreign data formats (ad,conllx,parse,ontonotes) to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp POSTaggerConverter help|ad|conllx|parse|ontonotes [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">expandME</td><td align="left">expandME</td><td align="left">Yes</td><td align="left">Expand multiword expressions.</td></tr><tr><td align="left">includeFeatures</td><td align="left">includeFeatures</td><td align="left">Yes</td><td align="left">Combine POS Tags with word features, like number and gender.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td rowspan="2" align="left" valign="middle">conllx</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td rowspan="2" align="left" valign="middle">parse</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr></tbody></table></div>

</div>

</div>

<div class="section" title="Lemmatizer"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.lemmatizer"></a>Lemmatizer</h2></div></div></div>


<div class="section" title="LemmatizerME"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.lemmatizer.LemmatizerME"></a>LemmatizerME</h3></div></div></div>


<p>Learnable lemmatizer</p>

<pre class="screen">

Usage: opennlp LemmatizerME model &lt; sentences


</pre> 
</div>

<div class="section" title="LemmatizerTrainerME"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.lemmatizer.LemmatizerTrainerME"></a>LemmatizerTrainerME</h3></div></div></div>


<p>Trainer for the learnable lemmatizer</p>

<pre class="screen">

Usage: opennlp LemmatizerTrainerME [-factory factoryName] [-params paramsFile] -lang language -model 
        modelFile -data sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of LemmatizerFactory where to get implementation and resources.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody></tbody></table></div>

</div>

<div class="section" title="LemmatizerEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.lemmatizer.LemmatizerEvaluator"></a>LemmatizerEvaluator</h3></div></div></div>


<p>Measures the performance of the Lemmatizer model with the reference data</p>

<pre class="screen">

Usage: opennlp LemmatizerEvaluator [-reportOutputFile outputFile] [-misclassified true|false] -model model 
        -data sampleData [-encoding charsetName] 
Arguments description:
	-reportOutputFile outputFile
		the path of the fine-grained report file.
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody></tbody></table></div>

</div>

</div>

<div class="section" title="Chunker"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.chunker"></a>Chunker</h2></div></div></div>


<div class="section" title="ChunkerME"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.chunker.ChunkerME"></a>ChunkerME</h3></div></div></div>


<p>Learnable chunker</p>

<pre class="screen">

Usage: opennlp ChunkerME model &lt; sentences


</pre> 
</div>

<div class="section" title="ChunkerTrainerME"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.chunker.ChunkerTrainerME"></a>ChunkerTrainerME</h3></div></div></div>


<p>Trainer for the learnable chunker</p>

<pre class="screen">

Usage: opennlp ChunkerTrainerME[.ad] [-factory factoryName] [-params paramsFile] -lang language -model 
        modelFile -data sampleData [-encoding charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of ChunkerFactory where to get implementation and resources.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">end</td><td align="left">end</td><td align="left">Yes</td><td align="left">Index of last sentence</td></tr><tr><td align="left">start</td><td align="left">start</td><td align="left">Yes</td><td align="left">Index of first sentence</td></tr></tbody></table></div>

</div>

<div class="section" title="ChunkerEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.chunker.ChunkerEvaluator"></a>ChunkerEvaluator</h3></div></div></div>


<p>Measures the performance of the Chunker model with the reference data</p>

<pre class="screen">

Usage: opennlp ChunkerEvaluator[.ad] [-misclassified true|false] -model model [-detailedF true|false] -data 
        sampleData [-encoding charsetName] 
Arguments description:
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-detailedF true|false
		if true will print detailed FMeasure results.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">end</td><td align="left">end</td><td align="left">Yes</td><td align="left">Index of last sentence</td></tr><tr><td align="left">start</td><td align="left">start</td><td align="left">Yes</td><td align="left">Index of first sentence</td></tr></tbody></table></div>

</div>

<div class="section" title="ChunkerCrossValidator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.chunker.ChunkerCrossValidator"></a>ChunkerCrossValidator</h3></div></div></div>


<p>K-fold cross validator for the chunker</p>

<pre class="screen">

Usage: opennlp ChunkerCrossValidator[.ad] [-factory factoryName] [-params paramsFile] -lang language 
        [-misclassified true|false] [-folds num] [-detailedF true|false] -data sampleData [-encoding 
        charsetName] 
Arguments description:
	-factory factoryName
		A sub-class of ChunkerFactory where to get implementation and resources.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-misclassified true|false
		if true will print false negatives and false positives.
	-folds num
		number of folds, default is 10.
	-detailedF true|false
		if true will print detailed FMeasure results.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">end</td><td align="left">end</td><td align="left">Yes</td><td align="left">Index of last sentence</td></tr><tr><td align="left">start</td><td align="left">start</td><td align="left">Yes</td><td align="left">Index of first sentence</td></tr></tbody></table></div>

</div>

<div class="section" title="ChunkerConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.chunker.ChunkerConverter"></a>ChunkerConverter</h3></div></div></div>


<p>Converts ad data format to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp ChunkerConverter help|ad [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td rowspan="5" align="left" valign="middle">ad</td><td align="left">encoding</td><td align="left">charsetName</td><td align="left">No</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr><tr><td align="left">lang</td><td align="left">language</td><td align="left">No</td><td align="left">Language which is being processed.</td></tr><tr><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">end</td><td align="left">end</td><td align="left">Yes</td><td align="left">Index of last sentence</td></tr><tr><td align="left">start</td><td align="left">start</td><td align="left">Yes</td><td align="left">Index of first sentence</td></tr></tbody></table></div>

</div>

</div>

<div class="section" title="Parser"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.parser"></a>Parser</h2></div></div></div>


<div class="section" title="Parser"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.Parser"></a>Parser</h3></div></div></div>


<p>Performs full syntactic parsing</p>

<pre class="screen">

Usage: opennlp Parser [-bs n -ap n -k n] model &lt; sentences 
-bs n: Use a beam size of n.
-ap f: Advance outcomes in with at least f% of the probability mass.
-k n: Show the top n parses.  This will also display their log-probablities.


</pre> 
</div>

<div class="section" title="ParserTrainer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.ParserTrainer"></a>ParserTrainer</h3></div></div></div>


<p>Trains the learnable parser</p>

<pre class="screen">

Usage: opennlp ParserTrainer[.ontonotes|.frenchtreebank] [-fun true|false] [-headRulesSerializerImpl 
        className] -headRules headRulesFile [-parserType CHUNKING|TREEINSERT] [-params paramsFile] -lang 
        language -model modelFile [-encoding charsetName] -data sampleData 
Arguments description:
	-fun true|false
		Learn to generate function tags.
	-headRulesSerializerImpl className
		head rules artifact serializer class name
	-headRules headRulesFile
		head rules file.
	-parserType CHUNKING|TREEINSERT
		one of CHUNKING or TREEINSERT, default is CHUNKING.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-model modelFile
		output model file.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.
	-data sampleData
		data to be used, usually a file name.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="2" align="left" valign="middle">frenchtreebank</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="ParserEvaluator"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.ParserEvaluator"></a>ParserEvaluator</h3></div></div></div>


<p>Measures the performance of the Parser model with the reference data</p>

<pre class="screen">

Usage: opennlp ParserEvaluator[.ontonotes|.frenchtreebank] [-misclassified true|false] -model model -data 
        sampleData [-encoding charsetName] 
Arguments description:
	-misclassified true|false
		if true will print false negatives and false positives.
	-model model
		the model file to be evaluated.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="2" align="left" valign="middle">frenchtreebank</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="ParserConverter"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.ParserConverter"></a>ParserConverter</h3></div></div></div>


<p>Converts foreign data formats (ontonotes,frenchtreebank) to native OpenNLP format</p>

<pre class="screen">

Usage: opennlp ParserConverter help|ontonotes|frenchtreebank [help|options...]


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="2" align="left" valign="middle">frenchtreebank</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="BuildModelUpdater"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.BuildModelUpdater"></a>BuildModelUpdater</h3></div></div></div>


<p>Trains and updates the build model in a parser model</p>

<pre class="screen">

Usage: opennlp BuildModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang 
        language -data sampleData [-encoding charsetName] 
Arguments description:
	-model modelFile
		output model file.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="2" align="left" valign="middle">frenchtreebank</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="CheckModelUpdater"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.CheckModelUpdater"></a>CheckModelUpdater</h3></div></div></div>


<p>Trains and updates the check model in a parser model</p>

<pre class="screen">

Usage: opennlp CheckModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang 
        language -data sampleData [-encoding charsetName] 
Arguments description:
	-model modelFile
		output model file.
	-params paramsFile
		training parameters file.
	-lang language
		language which is being processed.
	-data sampleData
		data to be used, usually a file name.
	-encoding charsetName
		encoding for reading and writing text, if absent the system default is used.


</pre> 
<p>The supported formats and arguments are:</p>

<div class="informaltable"><table border="1"><colgroup><col><col><col><col></colgroup><thead><tr><th align="left">Format</th><th align="left">Argument</th><th align="left">Value</th><th align="left">Optional</th><th align="left">Description</th></tr></thead><tbody><tr><td align="left" valign="middle">ontonotes</td><td align="left">ontoNotesDir</td><td align="left">OntoNotes 4.0 corpus directory</td><td align="left">No</td><td align="left">&nbsp;</td></tr><tr><td rowspan="2" align="left" valign="middle">frenchtreebank</td><td align="left">data</td><td align="left">sampleData</td><td align="left">No</td><td align="left">Data to be used, usually a file name.</td></tr><tr><td align="left">encoding</td><td align="left">charsetName</td><td align="left">Yes</td><td align="left">Encoding for reading and writing text, if absent the system default is used.</td></tr></tbody></table></div>

</div>

<div class="section" title="TaggerModelReplacer"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.parser.TaggerModelReplacer"></a>TaggerModelReplacer</h3></div></div></div>


<p>Replaces the tagger model in a parser model</p>

<pre class="screen">

Usage: opennlp TaggerModelReplacer parser.model tagger.model


</pre> 
</div>

</div>

<div class="section" title="Entitylinker"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="tools.cli.entitylinker"></a>Entitylinker</h2></div></div></div>


<div class="section" title="EntityLinker"><div class="titlepage"><div><div><h3 class="title"><a name="tools.cli.entitylinker.EntityLinker"></a>EntityLinker</h3></div></div></div>


<p>Links an entity to an external data set</p>

<pre class="screen">

Usage: opennlp EntityLinker model &lt; sentences


</pre> 
</div>

</div>


</div>
</div></body></html>