opennlp-wsd/src/main/java/opennlp/tools/disambiguator/FeaturesExtractor.java

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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 * 
 *   http://www.apache.org/licenses/LICENSE-2.0
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 * Unless required by applicable law or agreed to in writing,
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 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
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package opennlp.tools.disambiguator;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;

import opennlp.tools.disambiguator.ims.WTDIMS;

/**
 * Class for the extraction of features for the different Supervised
 * Disambiguation approaches.<br>
 * Each set of methods refer to one approach
 * <ul>
 * <li>IMS (It Makes Sense): check {@link https
 * ://www.comp.nus.edu.sg/~nght/pubs/ims.pdf} for details about this approach</li>
 * <li>SST (SuperSense Tagging): check {@link http
 * ://ttic.uchicago.edu/~altun/pubs/CiaAlt_EMNLP06.pdf} for details about this
 * approach</li>
 * </ul>
 * 
 * The first methods serve to extract the features for the algorithm IMS. Three
 * families of features are to be extracted: - PoS of Surrounding Words: it
 * requires one parameter: "Window size" - Surrounding Words: no parameters are
 * required - Local Collocations: it requires one parameter: "the n-gram"
 * 
 * check {@link https://www.comp.nus.edu.sg/~nght/pubs/ims.pdf} for details
 * about this approach
 */
public class FeaturesExtractor {

  public FeaturesExtractor() {
    super();
  }

  // IMS

  private String[] extractPosOfSurroundingWords(WTDIMS wordToDisambiguate,
      int windowSize) {

    String[] taggedSentence = wordToDisambiguate.getPosTags();

    String[] tags = new String[2 * windowSize + 1];

    int j = 0;

    for (int i = wordToDisambiguate.getWordIndex() - windowSize; i < wordToDisambiguate
        .getWordIndex() + windowSize; i++) {
      if (i < 0 || i >= wordToDisambiguate.getSentence().length) {
        tags[j] = "null";
      } else {
        tags[j] = taggedSentence[i].toLowerCase();
      }
      j++;
    }

    return tags;
  }

  private String[] extractSurroundingWords(WTDIMS wordToDisambiguate) {

    ArrayList<String> contextWords = new ArrayList<String>();

    for (int i = 0; i < wordToDisambiguate.getSentence().length; i++) {
      if (wordToDisambiguate.getLemmas() != null) {
        if (!WSDHelper.stopWords.contains(wordToDisambiguate.getSentence()[i]
            .toLowerCase()) && (wordToDisambiguate.getWordIndex() != i)) {

          String lemma = wordToDisambiguate.getLemmas()[i].toLowerCase()
              .replaceAll("[^a-z_]", "").trim();

          if (lemma.length() > 1) {
            contextWords.add(lemma);
          }

        }
      }
    }

    return contextWords.toArray(new String[contextWords.size()]);
  }

  private String[] extractLocalCollocations(WTDIMS wordToDisambiguate, int ngram) {
    /**
     * Here the author used only 11 features of this type. the range was set to
     * 3 (bigrams extracted in a way that they are at max separated by 1 word).
     */

    ArrayList<String> localCollocations = new ArrayList<String>();

    for (int i = wordToDisambiguate.getWordIndex() - ngram; i <= wordToDisambiguate
        .getWordIndex() + ngram; i++) {

      if (!(i < 0 || i > wordToDisambiguate.getSentence().length - 3)) {
        if ((i != wordToDisambiguate.getWordIndex())
            && (i + 1 != wordToDisambiguate.getWordIndex())
            && (i + 1 < wordToDisambiguate.getWordIndex() + ngram)) {
          String lc = (wordToDisambiguate.getSentence()[i] + " " + wordToDisambiguate
              .getSentence()[i + 1]).toLowerCase();
          localCollocations.add(lc);
        }
        if ((i != wordToDisambiguate.getWordIndex())
            && (i + 2 != wordToDisambiguate.getWordIndex())
            && (i + 2 < wordToDisambiguate.getWordIndex() + ngram)) {
          String lc = (wordToDisambiguate.getSentence()[i] + " " + wordToDisambiguate
              .getSentence()[i + 2]).toLowerCase();
          localCollocations.add(lc);
        }
      }

    }

    String[] res = new String[localCollocations.size()];
    res = localCollocations.toArray(res);

    return res;
  }

  /**
   * This methods generates the full list of Surrounding words, from the
   * training data. These data will be later used for the generation of the
   * features qualified of "Surrounding words
   * 
   * @param trainingData
   *          list of the training samples (type {@link WTDIMS}
   * @return the list of all the surrounding words from all the training data
   */
  public ArrayList<String> extractTrainingSurroundingWords(
      ArrayList<WTDIMS> trainingData) {
    
    HashMap<String, Object> words = new HashMap<String, Object>();
    
    for (WTDIMS word : trainingData) {
      for (String sWord : word.getSurroundingWords()) {
        if (!words.containsKey(sWord.toLowerCase()));
        words.put(sWord.toLowerCase(), null);
      }
    }

    ArrayList<String> list = new ArrayList<String>();

    for (String word : words.keySet()) {
        list.add(word);
    }

    return list;

  }

  /**
   * This method generates the different set of features related to the IMS
   * approach and store them in the corresponding attributes of the WTDIMS
   * 
   * @param wordToDisambiguate
   *          the word to disambiguate [object: WTDIMS]
   * @param windowSize
   *          the parameter required to generate the features qualified of
   *          "PoS of Surrounding Words"
   * @param ngram
   *          the parameter required to generate the features qualified of
   *          "Local Collocations"
   */
  public void extractIMSFeatures(WTDIMS wordToDisambiguate, int windowSize,
      int ngram) {

    wordToDisambiguate.setPosOfSurroundingWords(extractPosOfSurroundingWords(
        wordToDisambiguate, windowSize));
    wordToDisambiguate
        .setSurroundingWords(extractSurroundingWords(wordToDisambiguate));
    wordToDisambiguate.setLocalCollocations(extractLocalCollocations(
        wordToDisambiguate, ngram));

  }

  /**
   * This generates the context of IMS. It supposes that the features have
   * already been extracted and stored in the WTDIMS object, therefore it
   * doesn't require any parameters.
   * 
   * @param word
   *          the word to disambiguate
   * @param listSurrWords
   *          the full list of surrounding words of the training data
   * @return the Context of the wordToDisambiguate
   */
  public void serializeIMSFeatures(WTDIMS word, ArrayList<String> listSurrWords) {

    String[] posOfSurroundingWords = word.getPosOfSurroundingWords();
    ArrayList<String> surroundingWords = new ArrayList<String>(
        Arrays.asList((word.getSurroundingWords())));
    String[] localCollocations = word.getLocalCollocations();

    String[] serializedFeatures = new String[posOfSurroundingWords.length
        + localCollocations.length + listSurrWords.size()];

    int i = 0;

    for (String feature : posOfSurroundingWords) {
      serializedFeatures[i] = "F" + i + "=" + feature;
      i++;
    }

    for (String feature : localCollocations) {
      serializedFeatures[i] = "F" + i + "=" + feature;
      i++;
    }

    for (String feature : listSurrWords) {
      serializedFeatures[i] = "F" + i + "=0";
      if (surroundingWords.contains(feature)) {
        serializedFeatures[i] = "F" + i + "=1";
      }
      i++;

    }

    word.setFeatures(serializedFeatures);

  }

  // SST approach

}