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Searching by words from the same root
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If we were performing a query for the term craftsmen, would we also want documents returned that contained terms like crafted, craftier, craftily, crafts, craft, or crafty More than likely we would, but based on what we ve discussed so far, this would not happen. That s where stemmers come in handy.
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Analyzers: doors to flexibility
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A little bit of history
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While the Porter stemming algorithm is the best-known stemming algorithm in the Lucene community thanks to the widespread usage of the Snowball language written by Dr. Porter, it isn t the first. The first-published stemming algorithm was the Lovins stemming algorithm by Julie Beth Lovins in 1968. See http://snowball.tartarus.org/ algorithms/lovins/stemmer.html for some additional information. The Google search engine started using stemming technology in 2003.
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In 1979, Martin Porter designed an algorithm for reducing words to their root by removing their suffixes. This became known as the Porter stemming algorithm. The Porter stemming algorithm website is http://tartarus.org/~martin/PorterStemmer/ index.html, and it has a link to the original algorithm paper along with links to the algorithm written in 22 programming languages, including Java, Perl, Ruby, C#, and even ERLANG. In the first phase, rules are applied as in table 5.3. Remember that these rules are applied in sequence. That s why the SS rule is applied before the S rule. If the rules were not applied in that order, the S rule could change things in a way that we would not want it to. The application of later rules deals with the length of the word to ensure that the matching portion is indeed a suffix and not so much a part of the word that it would lose its meaning if the supposed suffix were removed. For example, take the words abatement and cement. If we remove the suffix ment from them, we re left with abate and ce. Clearly, cement has lost its meaning and would not be stemmed. The Lucene project has links to the Snowball stemming language, also developed by Porter. It s named in honor of the early (circa 1960s) string-manipulation programming language SNOBOL and is located at http://snowball.tartarus.org. Snowball is not just for the English language. The website has discussions of the algorithm in more than 15 languages, and it would be good for you to get involved if your native language is not listed there. Also, in the Contribution section, Lucene includes classes that perform the stemming functions along with a precompiled .jar file that can easily
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Table 5.3 The first rule set in the sequential process of stemming a word Rule SSES IES SS S -> -> -> -> SS I SS Caresses Ponies Caress Cats -> -> -> -> Example Caress Poni Caress Cat
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Indexing: where, how, what, and when
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be added to your application library. The JAR is located at lucene_install_directory/ contrib/snowball/lucene-snowball-*.jar. A SnowballPorterFilterFactory is available in Solr. Let s see how to use the stemmer analyzer (listing 5.12).
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Listing 5.12 Configure the Snowball filter for English
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@Entity @Indexed @AnalyzerDef( name="englishSnowball", tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class ), filters = { @TokenFilterDef(factory=StandardFilterFactory.class), @TokenFilterDef(factory=LowerCaseFilterFactory.class), @TokenFilterDef(factory = StopFilterFactory.class, params = @Parameter(name="words", value="com/manning/hsia/dvdstore /stopwords.txt") ), Use the @TokenFilterDef( Snowball filter factory = SnowballPorterFilterFactory.class, params = @Parameter(name="language", value="English") ) Define the } ) language public class Item { @Fields( { @Field(name="title"), @Field(name="title_stemmer", analyzer=@Analyzer(definition="englishSnowball")) }) title_stemmer uses private String title; the Snowball filter ... }
Hibernate Search uses the SnowballPorterFilterFactory B when indexing. It defines the language targeted C (Danish, Dutch, English, Finnish, French, German, German2, Italian, Kp, Lovins (the first published stemming algorithm), Norwegian, Porter (original implementation of the Porter Stemming algorithm), Portuguese, Russian, Spanish, or Swedish). The default is English. Listing 5.13 is an example of how to employ the Snowball analyzer to stem words during both the index build phase and the query phase. Don t focus here on how to write a query; we ll come to it in chapters 6 and 7.
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