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      Apache Lucene ICU integration module
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<p>
This module exposes functionality from 
<a href="http://site.icu-project.org/">ICU</a> to Apache Lucene. ICU4J is a Java
library that enhances Java's internationalization support by improving 
performance, keeping current with the Unicode Standard, and providing richer
APIs. 
<p>
For an introduction to Lucene's analysis API, see the {@link org.apache.lucene.analysis} package documentation.
<p>
This module exposes the following functionality:
</p>
<ul>
  <li><a href="#segmentation">Text Segmentation</a>: Tokenizes text based on 
  properties and rules defined in Unicode.</li>
  <li><a href="#collation">Collation</a>: Compare strings according to the 
  conventions and standards of a particular language, region or country.</li>
  <li><a href="#normalization">Normalization</a>: Converts text to a unique,
  equivalent form.</li>
  <li><a href="#casefolding">Case Folding</a>: Removes case distinctions with
  Unicode's Default Caseless Matching algorithm.</li>
  <li><a href="#searchfolding">Search Term Folding</a>: Removes distinctions
  (such as accent marks) between similar characters for a loose or fuzzy search.</li>
  <li><a href="#transform">Text Transformation</a>: Transforms Unicode text in
  a context-sensitive fashion: e.g. mapping Traditional to Simplified Chinese</li>
</ul>
<hr/>
<h1><a name="segmentation">Text Segmentation</a></h1>
<p>
Text Segmentation (Tokenization) divides document and query text into index terms
(typically words). Unicode provides special properties and rules so that this can
be done in a manner that works well with most languages.
</p>
<p>
Text Segmentation implements the word segmentation specified in
<a href="http://unicode.org/reports/tr29/">Unicode Text Segmentation</a>.
Additionally the algorithm can be tailored based on writing system, for example
text in the Thai script is automatically delegated to a dictionary-based segmentation 
algorithm.
</p>
<h2>Use Cases</h2>
<ul>
  <li>
    As a more thorough replacement for StandardTokenizer that works well for
    most languages. 
  </li>
</ul>
<h2>Example Usages</h2>
<h3>Tokenizing multilanguage text</h3>
<pre class="prettyprint">
  /**
   * This tokenizer will work well in general for most languages.
   */
  Tokenizer tokenizer = new ICUTokenizer(reader);
</pre>
<hr/>
<h1><a name="collation">Collation</a></h1>
<p>
  <code>ICUCollationKeyAnalyzer</code>
  converts each token into its binary <code>CollationKey</code> using the 
  provided <code>Collator</code>, allowing it to be 
  stored as an index term.
</p>
<p>
  <code>ICUCollationKeyAnalyzer</code> depends on ICU4J to produce the 
  <code>CollationKey</code>s.
</p>

<h2>Use Cases</h2>

<ul>
  <li>
    Efficient sorting of terms in languages that use non-Unicode character 
    orderings.  (Lucene Sort using a Locale can be very slow.) 
  </li>
  <li>
    Efficient range queries over fields that contain terms in languages that 
    use non-Unicode character orderings.  (Range queries using a Locale can be
    very slow.)
  </li>
  <li>
    Effective Locale-specific normalization (case differences, diacritics, etc.).
    ({@link org.apache.lucene.analysis.LowerCaseFilter} and 
    {@link org.apache.lucene.analysis.miscellaneous.ASCIIFoldingFilter} provide these services
    in a generic way that doesn't take into account locale-specific needs.)
  </li>
</ul>

<h2>Example Usages</h2>

<h3>Farsi Range Queries</h3>
<pre class="prettyprint">
  Collator collator = Collator.getInstance(new ULocale("ar"));
  ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(collator);
  RAMDirectory ramDir = new RAMDirectory();
  IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(analyzer));
  Document doc = new Document();
  doc.add(new Field("content", "\u0633\u0627\u0628", 
                    Field.Store.YES, Field.Index.ANALYZED));
  writer.addDocument(doc);
  writer.close();
  IndexSearcher is = new IndexSearcher(ramDir, true);

  QueryParser aqp = new QueryParser("content", analyzer);
  aqp.setAnalyzeRangeTerms(true);
    
  // Unicode order would include U+0633 in [ U+062F - U+0698 ], but Farsi
  // orders the U+0698 character before the U+0633 character, so the single
  // indexed Term above should NOT be returned by a ConstantScoreRangeQuery
  // with a Farsi Collator (or an Arabic one for the case when Farsi is not
  // supported).
  ScoreDoc[] result
    = is.search(aqp.parse("[ \u062F TO \u0698 ]"), null, 1000).scoreDocs;
  assertEquals("The index Term should not be included.", 0, result.length);
</pre>

<h3>Danish Sorting</h3>
<pre class="prettyprint">
  Analyzer analyzer 
    = new ICUCollationKeyAnalyzer(Collator.getInstance(new ULocale("da", "dk")));
  RAMDirectory indexStore = new RAMDirectory();
  IndexWriter writer = new IndexWriter(indexStore, new IndexWriterConfig(analyzer));
  String[] tracer = new String[] { "A", "B", "C", "D", "E" };
  String[] data = new String[] { "HAT", "HUT", "H\u00C5T", "H\u00D8T", "HOT" };
  String[] sortedTracerOrder = new String[] { "A", "E", "B", "D", "C" };
  for (int i = 0 ; i < data.length ; ++i) {
    Document doc = new Document();
    doc.add(new Field("tracer", tracer[i], Field.Store.YES, Field.Index.NO));
    doc.add(new Field("contents", data[i], Field.Store.NO, Field.Index.ANALYZED));
    writer.addDocument(doc);
  }
  writer.close();
  IndexSearcher searcher = new IndexSearcher(indexStore, true);
  Sort sort = new Sort();
  sort.setSort(new SortField("contents", SortField.STRING));
  Query query = new MatchAllDocsQuery();
  ScoreDoc[] result = searcher.search(query, null, 1000, sort).scoreDocs;
  for (int i = 0 ; i < result.length ; ++i) {
    Document doc = searcher.doc(result[i].doc);
    assertEquals(sortedTracerOrder[i], doc.getValues("tracer")[0]);
  }
</pre>

<h3>Turkish Case Normalization</h3>
<pre class="prettyprint">
  Collator collator = Collator.getInstance(new ULocale("tr", "TR"));
  collator.setStrength(Collator.PRIMARY);
  Analyzer analyzer = new ICUCollationKeyAnalyzer(collator);
  RAMDirectory ramDir = new RAMDirectory();
  IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(analyzer));
  Document doc = new Document();
  doc.add(new Field("contents", "DIGY", Field.Store.NO, Field.Index.ANALYZED));
  writer.addDocument(doc);
  writer.close();
  IndexSearcher is = new IndexSearcher(ramDir, true);
  QueryParser parser = new QueryParser("contents", analyzer);
  Query query = parser.parse("d\u0131gy");   // U+0131: dotless i
  ScoreDoc[] result = is.search(query, null, 1000).scoreDocs;
  assertEquals("The index Term should be included.", 1, result.length);
</pre>

<h2>Caveats and Comparisons</h2>
<p>
  <strong>WARNING:</strong> Make sure you use exactly the same 
  <code>Collator</code> at index and query time -- <code>CollationKey</code>s
  are only comparable when produced by
  the same <code>Collator</code>.  Since {@link java.text.RuleBasedCollator}s
  are not independently versioned, it is unsafe to search against stored
  <code>CollationKey</code>s unless the following are exactly the same (best 
  practice is to store this information with the index and check that they
  remain the same at query time):
</p>
<ol>
  <li>JVM vendor</li>
  <li>JVM version, including patch version</li>
  <li>
    The language (and country and variant, if specified) of the Locale
    used when constructing the collator via
    {@link java.text.Collator#getInstance(java.util.Locale)}.
  </li>
  <li>
    The collation strength used - see {@link java.text.Collator#setStrength(int)}
  </li>
</ol> 
<p>
  <code>ICUCollationKeyAnalyzer</code> uses ICU4J's <code>Collator</code>, which 
  makes its version available, thus allowing collation to be versioned
  independently from the JVM.  <code>ICUCollationKeyAnalyzer</code> is also 
  significantly faster and generates significantly shorter keys than 
  <code>CollationKeyAnalyzer</code>.  See
  <a href="http://site.icu-project.org/charts/collation-icu4j-sun"
    >http://site.icu-project.org/charts/collation-icu4j-sun</a> for key
  generation timing and key length comparisons between ICU4J and
  <code>java.text.Collator</code> over several languages.
</p>
<p>
  <code>CollationKey</code>s generated by <code>java.text.Collator</code>s are 
  not compatible with those those generated by ICU Collators.  Specifically, if
  you use <code>CollationKeyAnalyzer</code> to generate index terms, do not use
  <code>ICUCollationKeyAnalyzer</code> on the query side, or vice versa.
</p>
<hr/>
<h1><a name="normalization">Normalization</a></h1>
<p>
  <code>ICUNormalizer2Filter</code> normalizes term text to a 
  <a href="http://unicode.org/reports/tr15/">Unicode Normalization Form</a>, so 
  that <a href="http://en.wikipedia.org/wiki/Unicode_equivalence">equivalent</a>
  forms are standardized to a unique form.
</p>
<h2>Use Cases</h2>
<ul>
  <li> Removing differences in width for Asian-language text. 
  </li>
  <li> Standardizing complex text with non-spacing marks so that characters are 
  ordered consistently.
  </li>
</ul>
<h2>Example Usages</h2>
<h3>Normalizing text to NFC</h3>
<pre class="prettyprint">
  /**
   * Normalizer2 objects are unmodifiable and immutable.
   */
  Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
  /**
   * This filter will normalize to NFC.
   */
  TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, normalizer);
</pre>
<hr/>
<h1><a name="casefolding">Case Folding</a></h1>
<p>
Default caseless matching, or case-folding is more than just conversion to
lowercase. For example, it handles cases such as the Greek sigma, so that
"Μάϊος" and "ΜΆΪΟΣ" will match correctly.
</p>
<p>
Case-folding is still only an approximation of the language-specific rules
governing case. If the specific language is known, consider using
ICUCollationKeyFilter and indexing collation keys instead. This implementation
performs the "full" case-folding specified in the Unicode standard, and this
may change the length of the term. For example, the German ß is case-folded
to the string 'ss'.
</p>
<p>
Case folding is related to normalization, and as such is coupled with it in
this integration. To perform case-folding, you use normalization with the form
"nfkc_cf" (which is the default).
</p>
<h2>Use Cases</h2>
<ul>
  <li>
    As a more thorough replacement for LowerCaseFilter that has good behavior
    for most languages.
  </li>
</ul>
<h2>Example Usages</h2>
<h3>Lowercasing text</h3>
<pre class="prettyprint">
  /**
   * This filter will case-fold and normalize to NFKC.
   */
  TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer);
</pre>
<hr/>
<h1><a name="searchfolding">Search Term Folding</a></h1>
<p>
Search term folding removes distinctions (such as accent marks) between 
similar characters. It is useful for a fuzzy or loose search.
</p>
<p>
Search term folding implements many of the foldings specified in
<a href="http://www.unicode.org/reports/tr30/tr30-4.html">Character Foldings</a>
as a special normalization form.  This folding applies NFKC, Case Folding, and
many character foldings recursively.
</p>
<h2>Use Cases</h2>
<ul>
  <li>
    As a more thorough replacement for ASCIIFoldingFilter and LowerCaseFilter 
    that applies the same ideas to many more languages. 
  </li>
</ul>
<h2>Example Usages</h2>
<h3>Removing accents</h3>
<pre class="prettyprint">
  /**
   * This filter will case-fold, remove accents and other distinctions, and
   * normalize to NFKC.
   */
  TokenStream tokenstream = new ICUFoldingFilter(tokenizer);
</pre>
<hr/>
<h1><a name="transform">Text Transformation</a></h1>
<p>
ICU provides text-transformation functionality via its Transliteration API. This allows
you to transform text in a variety of ways, taking context into account.
</p>
<p>
For more information, see the 
<a href="http://userguide.icu-project.org/transforms/general">User's Guide</a>
and 
<a href="http://userguide.icu-project.org/transforms/general/rules">Rule Tutorial</a>.
</p>
<h2>Use Cases</h2>
<ul>
  <li>
    Convert Traditional to Simplified 
  </li>
  <li>
    Transliterate between different writing systems: e.g. Romanization
  </li>
</ul>
<h2>Example Usages</h2>
<h3>Convert Traditional to Simplified</h3>
<pre class="prettyprint">
  /**
   * This filter will map Traditional Chinese to Simplified Chinese
   */
  TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Traditional-Simplified"));
</pre>
<h3>Transliterate Serbian Cyrillic to Serbian Latin</h3>
<pre class="prettyprint">
  /**
   * This filter will map Serbian Cyrillic to Serbian Latin according to BGN rules
   */
  TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Serbian-Latin/BGN"));
</pre>
<hr/>
<h1><a name="backcompat">Backwards Compatibility</a></h1>
<p>
This module exists to provide up-to-date Unicode functionality that supports
the most recent version of Unicode (currently 8.0). However, some users who wish
for stronger backwards compatibility can restrict
{@link org.apache.lucene.analysis.icu.ICUNormalizer2Filter} to operate on only
a specific Unicode Version by using a {@link com.ibm.icu.text.FilteredNormalizer2}. 
</p>
<h2>Example Usages</h2>
<h3>Restricting normalization to Unicode 5.0</h3>
<pre class="prettyprint">
  /**
   * This filter will do NFC normalization, but will ignore any characters that
   * did not exist as of Unicode 5.0. Because of the normalization stability policy
   * of Unicode, this is an easy way to force normalization to a specific version.
   */
    Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
    UnicodeSet set = new UnicodeSet("[:age=5.0:]");
    // see FilteredNormalizer2 docs, the set should be frozen or performance will suffer
    set.freeze(); 
    FilteredNormalizer2 unicode50 = new FilteredNormalizer2(normalizer, set);
    TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, unicode50);
</pre>
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