/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file 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
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "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 under the License.
*/
/**
* A filter that decomposes compound words you find in many Germanic
* languages into the word parts. This example shows what it does:
* <table border="1" summary="example input stream">
* <tr>
* <th>Input token stream</th>
* </tr>
* <tr>
* <td>Rindfleischüberwachungsgesetz Drahtschere abba</td>
* </tr>
* </table>
* <br>
* <table border="1" summary="example output stream">
* <tr>
* <th>Output token stream</th>
* </tr>
* <tr>
* <td>(Rindfleischüberwachungsgesetz,0,29)</td>
* </tr>
* <tr>
* <td>(Rind,0,4,posIncr=0)</td>
* </tr>
* <tr>
* <td>(fleisch,4,11,posIncr=0)</td>
* </tr>
* <tr>
* <td>(überwachung,11,22,posIncr=0)</td>
* </tr>
* <tr>
* <td>(gesetz,23,29,posIncr=0)</td>
* </tr>
* <tr>
* <td>(Drahtschere,30,41)</td>
* </tr>
* <tr>
* <td>(Draht,30,35,posIncr=0)</td>
* </tr>
* <tr>
* <td>(schere,35,41,posIncr=0)</td>
* </tr>
* <tr>
* <td>(abba,42,46)</td>
* </tr>
* </table>
*
* The input token is always preserved and the filters do not alter the case of word parts. There are two variants of the
* filter available:
* <ul>
* <li><i>HyphenationCompoundWordTokenFilter</i>: it uses a
* hyphenation grammar based approach to find potential word parts of a
* given word.</li>
* <li><i>DictionaryCompoundWordTokenFilter</i>: it uses a
* brute-force dictionary-only based approach to find the word parts of a given
* word.</li>
* </ul>
*
* <h3>Compound word token filters</h3>
* <h4>HyphenationCompoundWordTokenFilter</h4>
* The {@link
* org.apache.lucene.analysis.compound.HyphenationCompoundWordTokenFilter
* HyphenationCompoundWordTokenFilter} uses hyphenation grammars to find
* potential subwords that a worth to check against the dictionary. It can be used
* without a dictionary as well but then produces a lot of "nonword" tokens.
* The quality of the output tokens is directly connected to the quality of the
* grammar file you use. For languages like German they are quite good.
* <h5>Grammar file</h5>
* Unfortunately we cannot bundle the hyphenation grammar files with Lucene
* because they do not use an ASF compatible license (they use the LaTeX
* Project Public License instead). You can find the XML based grammar
* files at the
* <a href="http://offo.sourceforge.net/hyphenation/index.html">Objects
* For Formatting Objects</a>
* (OFFO) Sourceforge project (direct link to download the pattern files:
* <a href="http://downloads.sourceforge.net/offo/offo-hyphenation.zip">http://downloads.sourceforge.net/offo/offo-hyphenation.zip</a>
* ). The files you need are in the subfolder
* <i>offo-hyphenation/hyph/</i>
* .
* <br>
* Credits for the hyphenation code go to the
* <a href="http://xmlgraphics.apache.org/fop/">Apache FOP project</a>
* .
*
* <h4>DictionaryCompoundWordTokenFilter</h4>
* The {@link
* org.apache.lucene.analysis.compound.DictionaryCompoundWordTokenFilter
* DictionaryCompoundWordTokenFilter} uses a dictionary-only approach to
* find subwords in a compound word. It is much slower than the one that
* uses the hyphenation grammars. You can use it as a first start to
* see if your dictionary is good or not because it is much simpler in design.
*
* <h3>Dictionary</h3>
* The output quality of both token filters is directly connected to the
* quality of the dictionary you use. They are language dependent of course.
* You always should use a dictionary
* that fits to the text you want to index. If you index medical text for
* example then you should use a dictionary that contains medical words.
* A good start for general text are the dictionaries you find at the
* <a href="http://wiki.services.openoffice.org/wiki/Dictionaries">OpenOffice
* dictionaries</a>
* Wiki.
*
* <h3>Which variant should I use?</h3>
* This decision matrix should help you:
* <table border="1" summary="comparison of dictionary and hyphenation based decompounding">
* <tr>
* <th>Token filter</th>
* <th>Output quality</th>
* <th>Performance</th>
* </tr>
* <tr>
* <td>HyphenationCompoundWordTokenFilter</td>
* <td>good if grammar file is good – acceptable otherwise</td>
* <td>fast</td>
* </tr>
* <tr>
* <td>DictionaryCompoundWordTokenFilter</td>
* <td>good</td>
* <td>slow</td>
* </tr>
* </table>
* <h3>Examples</h3>
* <pre class="prettyprint">
* public void testHyphenationCompoundWordsDE() throws Exception {
* String[] dict = { "Rind", "Fleisch", "Draht", "Schere", "Gesetz",
* "Aufgabe", "Überwachung" };
*
* Reader reader = new FileReader("de_DR.xml");
*
* HyphenationTree hyphenator = HyphenationCompoundWordTokenFilter
* .getHyphenationTree(reader);
*
* HyphenationCompoundWordTokenFilter tf = new HyphenationCompoundWordTokenFilter(
* new WhitespaceTokenizer(new StringReader(
* "Rindfleischüberwachungsgesetz Drahtschere abba")), hyphenator,
* dict, CompoundWordTokenFilterBase.DEFAULT_MIN_WORD_SIZE,
* CompoundWordTokenFilterBase.DEFAULT_MIN_SUBWORD_SIZE,
* CompoundWordTokenFilterBase.DEFAULT_MAX_SUBWORD_SIZE, false);
*
* CharTermAttribute t = tf.addAttribute(CharTermAttribute.class);
* while (tf.incrementToken()) {
* System.out.println(t);
* }
* }
*
* public void testHyphenationCompoundWordsWithoutDictionaryDE() throws Exception {
* Reader reader = new FileReader("de_DR.xml");
*
* HyphenationTree hyphenator = HyphenationCompoundWordTokenFilter
* .getHyphenationTree(reader);
*
* HyphenationCompoundWordTokenFilter tf = new HyphenationCompoundWordTokenFilter(
* new WhitespaceTokenizer(new StringReader(
* "Rindfleischüberwachungsgesetz Drahtschere abba")), hyphenator);
*
* CharTermAttribute t = tf.addAttribute(CharTermAttribute.class);
* while (tf.incrementToken()) {
* System.out.println(t);
* }
* }
*
* public void testDumbCompoundWordsSE() throws Exception {
* String[] dict = { "Bil", "Dörr", "Motor", "Tak", "Borr", "Slag", "Hammar",
* "Pelar", "Glas", "Ögon", "Fodral", "Bas", "Fiol", "Makare", "Gesäll",
* "Sko", "Vind", "Rute", "Torkare", "Blad" };
*
* DictionaryCompoundWordTokenFilter tf = new DictionaryCompoundWordTokenFilter(
* new WhitespaceTokenizer(
* new StringReader(
* "Bildörr Bilmotor Biltak Slagborr Hammarborr Pelarborr Glasögonfodral Basfiolsfodral Basfiolsfodralmakaregesäll Skomakare Vindrutetorkare Vindrutetorkarblad abba")),
* dict);
* CharTermAttribute t = tf.addAttribute(CharTermAttribute.class);
* while (tf.incrementToken()) {
* System.out.println(t);
* }
* }
* </pre>
*/
package org.apache.lucene.analysis.compound;