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14 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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16 limitations under the License.
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20 <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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23 <p>API and code to convert text into indexable/searchable tokens. Covers {@link Lucene.Net.Analysis.Analyzer} and related classes.</p>
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24 <h2>Parsing? Tokenization? Analysis!</h2>
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26 Lucene, indexing and search library, accepts only plain text input.
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30 Applications that build their search capabilities upon Lucene may support documents in various formats – HTML, XML, PDF, Word – just to name a few.
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31 Lucene does not care about the <i>Parsing</i> of these and other document formats, and it is the responsibility of the
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32 application using Lucene to use an appropriate <i>Parser</i> to convert the original format into plain text before passing that plain text to Lucene.
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34 <h2>Tokenization</h2>
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36 Plain text passed to Lucene for indexing goes through a process generally called tokenization. Tokenization is the process
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37 of breaking input text into small indexing elements – tokens.
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38 The way input text is broken into tokens heavily influences how people will then be able to search for that text.
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39 For instance, sentences beginnings and endings can be identified to provide for more accurate phrase
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40 and proximity searches (though sentence identification is not provided by Lucene).
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42 In some cases simply breaking the input text into tokens is not enough – a deeper <i>Analysis</i> may be needed.
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43 There are many post tokenization steps that can be done, including (but not limited to):
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45 <li><a href = "http://en.wikipedia.org//wiki/Stemming">Stemming</a> –
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46 Replacing of words by their stems.
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47 For instance with English stemming "bikes" is replaced by "bike";
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48 now query "bike" can find both documents containing "bike" and those containing "bikes".
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50 <li><a href = "http://en.wikipedia.org//wiki/Stop_words">Stop Words Filtering</a> –
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51 Common words like "the", "and" and "a" rarely add any value to a search.
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52 Removing them shrinks the index size and increases performance.
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53 It may also reduce some "noise" and actually improve search quality.
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55 <li><a href = "http://en.wikipedia.org//wiki/Text_normalization">Text Normalization</a> –
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56 Stripping accents and other character markings can make for better searching.
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58 <li><a href = "http://en.wikipedia.org//wiki/Synonym">Synonym Expansion</a> –
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59 Adding in synonyms at the same token position as the current word can mean better
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60 matching when users search with words in the synonym set.
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64 <h2>Core Analysis</h2>
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66 The analysis package provides the mechanism to convert Strings and Readers into tokens that can be indexed by Lucene. There
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67 are three main classes in the package from which all analysis processes are derived. These are:
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69 <li>{@link Lucene.Net.Analysis.Analyzer} – An Analyzer is responsible for building a {@link Lucene.Net.Analysis.TokenStream} which can be consumed
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70 by the indexing and searching processes. See below for more information on implementing your own Analyzer.</li>
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71 <li>{@link Lucene.Net.Analysis.Tokenizer} – A Tokenizer is a {@link Lucene.Net.Analysis.TokenStream} and is responsible for breaking
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72 up incoming text into tokens. In most cases, an Analyzer will use a Tokenizer as the first step in
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73 the analysis process.</li>
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74 <li>{@link Lucene.Net.Analysis.TokenFilter} – A TokenFilter is also a {@link Lucene.Net.Analysis.TokenStream} and is responsible
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75 for modifying tokens that have been created by the Tokenizer. Common modifications performed by a
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76 TokenFilter are: deletion, stemming, synonym injection, and down casing. Not all Analyzers require TokenFilters</li>
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78 <b>Lucene 2.9 introduces a new TokenStream API. Please see the section "New TokenStream API" below for more details.</b>
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80 <h2>Hints, Tips and Traps</h2>
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82 The synergy between {@link Lucene.Net.Analysis.Analyzer} and {@link Lucene.Net.Analysis.Tokenizer}
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83 is sometimes confusing. To ease on this confusion, some clarifications:
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85 <li>The {@link Lucene.Net.Analysis.Analyzer} is responsible for the entire task of
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86 <u>creating</u> tokens out of the input text, while the {@link Lucene.Net.Analysis.Tokenizer}
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87 is only responsible for <u>breaking</u> the input text into tokens. Very likely, tokens created
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88 by the {@link Lucene.Net.Analysis.Tokenizer} would be modified or even omitted
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89 by the {@link Lucene.Net.Analysis.Analyzer} (via one or more
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90 {@link Lucene.Net.Analysis.TokenFilter}s) before being returned.
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92 <li>{@link Lucene.Net.Analysis.Tokenizer} is a {@link Lucene.Net.Analysis.TokenStream},
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93 but {@link Lucene.Net.Analysis.Analyzer} is not.
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95 <li>{@link Lucene.Net.Analysis.Analyzer} is "field aware", but
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96 {@link Lucene.Net.Analysis.Tokenizer} is not.
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101 Lucene Java provides a number of analysis capabilities, the most commonly used one being the {@link
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102 Lucene.Net.Analysis.Standard.StandardAnalyzer}. Many applications will have a long and industrious life with nothing more
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103 than the StandardAnalyzer. However, there are a few other classes/packages that are worth mentioning:
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105 <li>{@link Lucene.Net.Analysis.PerFieldAnalyzerWrapper} – Most Analyzers perform the same operation on all
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106 {@link Lucene.Net.Documents.Field}s. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different
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107 {@link Lucene.Net.Documents.Field}s.</li>
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108 <li>The contrib/analyzers library located at the root of the Lucene distribution has a number of different Analyzer implementations to solve a variety
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109 of different problems related to searching. Many of the Analyzers are designed to analyze non-English languages.</li>
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110 <li>The contrib/snowball library
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111 located at the root of the Lucene distribution has Analyzer and TokenFilter
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112 implementations for a variety of Snowball stemmers.
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113 See <a href = "http://snowball.tartarus.org">http://snowball.tartarus.org</a>
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114 for more information on Snowball stemmers.</li>
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115 <li>There are a variety of Tokenizer and TokenFilter implementations in this package. Take a look around, chances are someone has implemented what you need.</li>
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119 Analysis is one of the main causes of performance degradation during indexing. Simply put, the more you analyze the slower the indexing (in most cases).
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120 Perhaps your application would be just fine using the simple {@link Lucene.Net.Analysis.WhitespaceTokenizer} combined with a
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121 {@link Lucene.Net.Analysis.StopFilter}. The contrib/benchmark library can be useful for testing out the speed of the analysis process.
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123 <h2>Invoking the Analyzer</h2>
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125 Applications usually do not invoke analysis – Lucene does it for them:
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127 <li>At indexing, as a consequence of
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128 {@link Lucene.Net.Index.IndexWriter#addDocument(Lucene.Net.Documents.Document) addDocument(doc)},
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129 the Analyzer in effect for indexing is invoked for each indexed field of the added document.
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131 <li>At search, as a consequence of
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132 {@link Lucene.Net.QueryParsers.QueryParser#parse(java.lang.String) QueryParser.parse(queryText)},
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133 the QueryParser may invoke the Analyzer in effect.
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134 Note that for some queries analysis does not take place, e.g. wildcard queries.
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137 However an application might invoke Analysis of any text for testing or for any other purpose, something like:
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139 Analyzer analyzer = new StandardAnalyzer(); // or any other analyzer
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140 TokenStream ts = analyzer.tokenStream("myfield",new StringReader("some text goes here"));
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141 while (ts.incrementToken()) {
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142 System.out.println("token: "+ts));
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147 <h2>Indexing Analysis vs. Search Analysis</h2>
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149 Selecting the "correct" analyzer is crucial
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150 for search quality, and can also affect indexing and search performance.
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151 The "correct" analyzer differs between applications.
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152 Lucene java's wiki page
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153 <a href = "http://wiki.apache.org//lucene-java/AnalysisParalysis">AnalysisParalysis</a>
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154 provides some data on "analyzing your analyzer".
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155 Here are some rules of thumb:
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157 <li>Test test test... (did we say test?)</li>
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158 <li>Beware of over analysis – might hurt indexing performance.</li>
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159 <li>Start with same analyzer for indexing and search, otherwise searches would not find what they are supposed to...</li>
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160 <li>In some cases a different analyzer is required for indexing and search, for instance:
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162 <li>Certain searches require more stop words to be filtered. (I.e. more than those that were filtered at indexing.)</li>
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163 <li>Query expansion by synonyms, acronyms, auto spell correction, etc.</li>
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165 This might sometimes require a modified analyzer – see the next section on how to do that.
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169 <h2>Implementing your own Analyzer</h2>
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170 <p>Creating your own Analyzer is straightforward. It usually involves either wrapping an existing Tokenizer and set of TokenFilters to create a new Analyzer
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171 or creating both the Analyzer and a Tokenizer or TokenFilter. Before pursuing this approach, you may find it worthwhile
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172 to explore the contrib/analyzers library and/or ask on the java-user@lucene.apache.org mailing list first to see if what you need already exists.
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173 If you are still committed to creating your own Analyzer or TokenStream derivation (Tokenizer or TokenFilter) have a look at
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174 the source code of any one of the many samples located in this package.
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177 The following sections discuss some aspects of implementing your own analyzer.
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179 <h3>Field Section Boundaries</h3>
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181 When {@link Lucene.Net.Documents.Document#add(Lucene.Net.Documents.Fieldable) document.add(field)}
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182 is called multiple times for the same field name, we could say that each such call creates a new
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183 section for that field in that document.
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184 In fact, a separate call to
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185 {@link Lucene.Net.Analysis.Analyzer#tokenStream(java.lang.String, java.io.Reader) tokenStream(field,reader)}
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186 would take place for each of these so called "sections".
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187 However, the default Analyzer behavior is to treat all these sections as one large section.
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188 This allows phrase search and proximity search to seamlessly cross
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189 boundaries between these "sections".
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190 In other words, if a certain field "f" is added like this:
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192 document.add(new Field("f","first ends",...);
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193 document.add(new Field("f","starts two",...);
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194 indexWriter.addDocument(document);
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196 Then, a phrase search for "ends starts" would find that document.
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197 Where desired, this behavior can be modified by introducing a "position gap" between consecutive field "sections",
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198 simply by overriding
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199 {@link Lucene.Net.Analysis.Analyzer#getPositionIncrementGap(java.lang.String) Analyzer.getPositionIncrementGap(fieldName)}:
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201 Analyzer myAnalyzer = new StandardAnalyzer() {
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202 public int getPositionIncrementGap(String fieldName) {
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208 <h3>Token Position Increments</h3>
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210 By default, all tokens created by Analyzers and Tokenizers have a
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211 {@link Lucene.Net.Analysis.Tokenattributes.PositionIncrementAttribute#getPositionIncrement() position increment} of one.
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212 This means that the position stored for that token in the index would be one more than
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213 that of the previous token.
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214 Recall that phrase and proximity searches rely on position info.
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217 If the selected analyzer filters the stop words "is" and "the", then for a document
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218 containing the string "blue is the sky", only the tokens "blue", "sky" are indexed,
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219 with position("sky") = 1 + position("blue"). Now, a phrase query "blue is the sky"
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220 would find that document, because the same analyzer filters the same stop words from
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221 that query. But also the phrase query "blue sky" would find that document.
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224 If this behavior does not fit the application needs,
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225 a modified analyzer can be used, that would increment further the positions of
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226 tokens following a removed stop word, using
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227 {@link Lucene.Net.Analysis.Tokenattributes.PositionIncrementAttribute#setPositionIncrement(int)}.
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228 This can be done with something like:
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230 public TokenStream tokenStream(final String fieldName, Reader reader) {
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231 final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
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232 TokenStream res = new TokenStream() {
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233 TermAttribute termAtt = (TermAttribute) addAttribute(TermAttribute.class);
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234 PositionIncrementAttribute posIncrAtt = (PositionIncrementAttribute) addAttribute(PositionIncrementAttribute.class);
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236 public boolean incrementToken() throws IOException {
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237 int extraIncrement = 0;
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239 boolean hasNext = ts.incrementToken();
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241 if (stopWords.contains(termAtt.term())) {
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242 extraIncrement++; // filter this word
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245 if (extraIncrement>0) {
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246 posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
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256 Now, with this modified analyzer, the phrase query "blue sky" would find that document.
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257 But note that this is yet not a perfect solution, because any phrase query "blue w1 w2 sky"
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258 where both w1 and w2 are stop words would match that document.
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261 Few more use cases for modifying position increments are:
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263 <li>Inhibiting phrase and proximity matches in sentence boundaries – for this, a tokenizer that
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264 identifies a new sentence can add 1 to the position increment of the first token of the new sentence.</li>
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265 <li>Injecting synonyms – here, synonyms of a token should be added after that token,
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266 and their position increment should be set to 0.
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267 As result, all synonyms of a token would be considered to appear in exactly the
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268 same position as that token, and so would they be seen by phrase and proximity searches.</li>
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271 <h2>New TokenStream API</h2>
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273 With Lucene 2.9 we introduce a new TokenStream API. The old API used to produce Tokens. A Token
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274 has getter and setter methods for different properties like positionIncrement and termText.
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275 While this approach was sufficient for the default indexing format, it is not versatile enough for
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276 Flexible Indexing, a term which summarizes the effort of making the Lucene indexer pluggable and extensible for custom
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280 A fully customizable indexer means that users will be able to store custom data structures on disk. Therefore an API
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281 is necessary that can transport custom types of data from the documents to the indexer.
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283 <h3>Attribute and AttributeSource</h3>
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284 Lucene 2.9 therefore introduces a new pair of classes called {@link Lucene.Net.Util.Attribute} and
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285 {@link Lucene.Net.Util.AttributeSource}. An Attribute serves as a
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286 particular piece of information about a text token. For example, {@link Lucene.Net.Analysis.Tokenattributes.TermAttribute}
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287 contains the term text of a token, and {@link Lucene.Net.Analysis.Tokenattributes.OffsetAttribute} contains the start and end character offsets of a token.
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288 An AttributeSource is a collection of Attributes with a restriction: there may be only one instance of each attribute type. TokenStream now extends AttributeSource, which
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289 means that one can add Attributes to a TokenStream. Since TokenFilter extends TokenStream, all filters are also
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292 Lucene now provides six Attributes out of the box, which replace the variables the Token class has:
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294 <li>{@link Lucene.Net.Analysis.Tokenattributes.TermAttribute}<p>The term text of a token.</p></li>
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295 <li>{@link Lucene.Net.Analysis.Tokenattributes.OffsetAttribute}<p>The start and end offset of token in characters.</p></li>
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296 <li>{@link Lucene.Net.Analysis.Tokenattributes.PositionIncrementAttribute}<p>See above for detailed information about position increment.</p></li>
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297 <li>{@link Lucene.Net.Analysis.Tokenattributes.PayloadAttribute}<p>The payload that a Token can optionally have.</p></li>
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298 <li>{@link Lucene.Net.Analysis.Tokenattributes.TypeAttribute}<p>The type of the token. Default is 'word'.</p></li>
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299 <li>{@link Lucene.Net.Analysis.Tokenattributes.FlagsAttribute}<p>Optional flags a token can have.</p></li>
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302 <h3>Using the new TokenStream API</h3>
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303 There are a few important things to know in order to use the new API efficiently which are summarized here. You may want
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304 to walk through the example below first and come back to this section afterwards.
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306 Please keep in mind that an AttributeSource can only have one instance of a particular Attribute. Furthermore, if
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307 a chain of a TokenStream and multiple TokenFilters is used, then all TokenFilters in that chain share the Attributes
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308 with the TokenStream.
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312 Attribute instances are reused for all tokens of a document. Thus, a TokenStream/-Filter needs to update
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313 the appropriate Attribute(s) in incrementToken(). The consumer, commonly the Lucene indexer, consumes the data in the
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314 Attributes and then calls incrementToken() again until it retuns false, which indicates that the end of the stream
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315 was reached. This means that in each call of incrementToken() a TokenStream/-Filter can safely overwrite the data in
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316 the Attribute instances.
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320 For performance reasons a TokenStream/-Filter should add/get Attributes during instantiation; i.e., create an attribute in the
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321 constructor and store references to it in an instance variable. Using an instance variable instead of calling addAttribute()/getAttribute()
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322 in incrementToken() will avoid expensive casting and attribute lookups for every token in the document.
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326 All methods in AttributeSource are idempotent, which means calling them multiple times always yields the same
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327 result. This is especially important to know for addAttribute(). The method takes the <b>type</b> (<code>Class</code>)
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328 of an Attribute as an argument and returns an <b>instance</b>. If an Attribute of the same type was previously added, then
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329 the already existing instance is returned, otherwise a new instance is created and returned. Therefore TokenStreams/-Filters
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330 can safely call addAttribute() with the same Attribute type multiple times. Even consumers of TokenStreams should
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331 normally call addAttribute() instead of getAttribute(), because it would not fail if the TokenStream does not have this
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332 Attribute (getAttribute() would throw an IllegalArgumentException, if the Attribute is missing). More advanced code
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333 could simply check with hasAttribute(), if a TokenStream has it, and may conditionally leave out processing for
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337 In this example we will create a WhiteSpaceTokenizer and use a LengthFilter to suppress all words that only
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338 have two or less characters. The LengthFilter is part of the Lucene core and its implementation will be explained
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339 here to illustrate the usage of the new TokenStream API.<br>
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340 Then we will develop a custom Attribute, a PartOfSpeechAttribute, and add another filter to the chain which
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341 utilizes the new custom attribute, and call it PartOfSpeechTaggingFilter.
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342 <h4>Whitespace tokenization</h4>
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344 public class MyAnalyzer extends Analyzer {
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346 public TokenStream tokenStream(String fieldName, Reader reader) {
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347 TokenStream stream = new WhitespaceTokenizer(reader);
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351 public static void main(String[] args) throws IOException {
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352 // text to tokenize
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353 final String text = "This is a demo of the new TokenStream API";
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355 MyAnalyzer analyzer = new MyAnalyzer();
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356 TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
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358 // get the TermAttribute from the TokenStream
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359 TermAttribute termAtt = (TermAttribute) stream.addAttribute(TermAttribute.class);
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363 // print all tokens until stream is exhausted
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364 while (stream.incrementToken()) {
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365 System.out.println(termAtt.term());
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373 In this easy example a simple white space tokenization is performed. In main() a loop consumes the stream and
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374 prints the term text of the tokens by accessing the TermAttribute that the WhitespaceTokenizer provides.
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375 Here is the output:
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387 <h4>Adding a LengthFilter</h4>
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388 We want to suppress all tokens that have 2 or less characters. We can do that easily by adding a LengthFilter
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389 to the chain. Only the tokenStream() method in our analyzer needs to be changed:
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391 public TokenStream tokenStream(String fieldName, Reader reader) {
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392 TokenStream stream = new WhitespaceTokenizer(reader);
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393 stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
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397 Note how now only words with 3 or more characters are contained in the output:
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406 Now let's take a look how the LengthFilter is implemented (it is part of Lucene's core):
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408 public final class LengthFilter extends TokenFilter {
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413 private TermAttribute termAtt;
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416 * Build a filter that removes words that are too long or too
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417 * short from the text.
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419 public LengthFilter(TokenStream in, int min, int max)
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424 termAtt = (TermAttribute) addAttribute(TermAttribute.class);
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428 * Returns the next input Token whose term() is the right len
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430 public final boolean incrementToken() throws IOException
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432 assert termAtt != null;
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433 // return the first non-stop word found
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434 while (input.incrementToken()) {
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435 int len = termAtt.termLength();
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436 if (len >= min && len <= max) {
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439 // note: else we ignore it but should we index each part of it?
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441 // reached EOS -- return null
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446 The TermAttribute is added in the constructor and stored in the instance variable <code>termAtt</code>.
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447 Remember that there can only be a single instance of TermAttribute in the chain, so in our example the
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448 <code>addAttribute()</code> call in LengthFilter returns the TermAttribute that the WhitespaceTokenizer already added. The tokens
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449 are retrieved from the input stream in the <code>incrementToken()</code> method. By looking at the term text
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450 in the TermAttribute the length of the term can be determined and too short or too long tokens are skipped.
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451 Note how <code>incrementToken()</code> can efficiently access the instance variable; no attribute lookup or downcasting
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452 is neccessary. The same is true for the consumer, which can simply use local references to the Attributes.
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454 <h4>Adding a custom Attribute</h4>
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455 Now we're going to implement our own custom Attribute for part-of-speech tagging and call it consequently
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456 <code>PartOfSpeechAttribute</code>. First we need to define the interface of the new Attribute:
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458 public interface PartOfSpeechAttribute extends Attribute {
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459 public static enum PartOfSpeech {
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460 Noun, Verb, Adjective, Adverb, Pronoun, Preposition, Conjunction, Article, Unknown
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463 public void setPartOfSpeech(PartOfSpeech pos);
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465 public PartOfSpeech getPartOfSpeech();
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469 Now we also need to write the implementing class. The name of that class is important here: By default, Lucene
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470 checks if there is a class with the name of the Attribute with the postfix 'Impl'. In this example, we would
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471 consequently call the implementing class <code>PartOfSpeechAttributeImpl</code>. <br/>
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472 This should be the usual behavior. However, there is also an expert-API that allows changing these naming conventions:
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473 {@link Lucene.Net.Util.AttributeSource.AttributeFactory}. The factory accepts an Attribute interface as argument
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474 and returns an actual instance. You can implement your own factory if you need to change the default behavior. <br/><br/>
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476 Now here is the actual class that implements our new Attribute. Notice that the class has to extend
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477 {@link Lucene.Net.Util.AttributeImpl}:
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480 public final class PartOfSpeechAttributeImpl extends AttributeImpl
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481 implements PartOfSpeechAttribute{
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483 private PartOfSpeech pos = PartOfSpeech.Unknown;
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485 public void setPartOfSpeech(PartOfSpeech pos) {
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489 public PartOfSpeech getPartOfSpeech() {
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493 public void clear() {
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494 pos = PartOfSpeech.Unknown;
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497 public void copyTo(AttributeImpl target) {
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498 ((PartOfSpeechAttributeImpl) target).pos = pos;
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501 public boolean equals(Object other) {
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502 if (other == this) {
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506 if (other instanceof PartOfSpeechAttributeImpl) {
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507 return pos == ((PartOfSpeechAttributeImpl) other).pos;
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513 public int hashCode() {
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514 return pos.ordinal();
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518 This is a simple Attribute implementation has only a single variable that stores the part-of-speech of a token. It extends the
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519 new <code>AttributeImpl</code> class and therefore implements its abstract methods <code>clear(), copyTo(), equals(), hashCode()</code>.
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520 Now we need a TokenFilter that can set this new PartOfSpeechAttribute for each token. In this example we show a very naive filter
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521 that tags every word with a leading upper-case letter as a 'Noun' and all other words as 'Unknown'.
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523 public static class PartOfSpeechTaggingFilter extends TokenFilter {
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524 PartOfSpeechAttribute posAtt;
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525 TermAttribute termAtt;
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527 protected PartOfSpeechTaggingFilter(TokenStream input) {
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529 posAtt = (PartOfSpeechAttribute) addAttribute(PartOfSpeechAttribute.class);
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530 termAtt = (TermAttribute) addAttribute(TermAttribute.class);
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533 public boolean incrementToken() throws IOException {
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534 if (!input.incrementToken()) {return false;}
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535 posAtt.setPartOfSpeech(determinePOS(termAtt.termBuffer(), 0, termAtt.termLength()));
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539 // determine the part of speech for the given term
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540 protected PartOfSpeech determinePOS(char[] term, int offset, int length) {
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541 // naive implementation that tags every uppercased word as noun
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542 if (length > 0 && Character.isUpperCase(term[0])) {
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543 return PartOfSpeech.Noun;
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545 return PartOfSpeech.Unknown;
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549 Just like the LengthFilter, this new filter accesses the attributes it needs in the constructor and
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550 stores references in instance variables. Notice how you only need to pass in the interface of the new
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551 Attribute and instantiating the correct class is automatically been taken care of.
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552 Now we need to add the filter to the chain:
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554 public TokenStream tokenStream(String fieldName, Reader reader) {
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555 TokenStream stream = new WhitespaceTokenizer(reader);
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556 stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
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557 stream = new PartOfSpeechTaggingFilter(stream);
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561 Now let's look at the output:
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570 Apparently it hasn't changed, which shows that adding a custom attribute to a TokenStream/Filter chain does not
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571 affect any existing consumers, simply because they don't know the new Attribute. Now let's change the consumer
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572 to make use of the new PartOfSpeechAttribute and print it out:
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574 public static void main(String[] args) throws IOException {
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575 // text to tokenize
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576 final String text = "This is a demo of the new TokenStream API";
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578 MyAnalyzer analyzer = new MyAnalyzer();
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579 TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
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581 // get the TermAttribute from the TokenStream
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582 TermAttribute termAtt = (TermAttribute) stream.addAttribute(TermAttribute.class);
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584 // get the PartOfSpeechAttribute from the TokenStream
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585 PartOfSpeechAttribute posAtt = (PartOfSpeechAttribute) stream.addAttribute(PartOfSpeechAttribute.class);
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589 // print all tokens until stream is exhausted
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590 while (stream.incrementToken()) {
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591 System.out.println(termAtt.term() + ": " + posAtt.getPartOfSpeech());
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598 The change that was made is to get the PartOfSpeechAttribute from the TokenStream and print out its contents in
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599 the while loop that consumes the stream. Here is the new output:
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608 Each word is now followed by its assigned PartOfSpeech tag. Of course this is a naive
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609 part-of-speech tagging. The word 'This' should not even be tagged as noun; it is only spelled capitalized because it
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610 is the first word of a sentence. Actually this is a good opportunity for an excerise. To practice the usage of the new
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611 API the reader could now write an Attribute and TokenFilter that can specify for each word if it was the first token
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612 of a sentence or not. Then the PartOfSpeechTaggingFilter can make use of this knowledge and only tag capitalized words
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613 as nouns if not the first word of a sentence (we know, this is still not a correct behavior, but hey, it's a good exercise).
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614 As a small hint, this is how the new Attribute class could begin:
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616 public class FirstTokenOfSentenceAttributeImpl extends Attribute
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617 implements FirstTokenOfSentenceAttribute {
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619 private boolean firstToken;
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621 public void setFirstToken(boolean firstToken) {
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622 this.firstToken = firstToken;
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625 public boolean getFirstToken() {
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629 public void clear() {
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630 firstToken = false;
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projects / mono.git / blob