--- /dev/null
+<!doctype html public "-//w3c//dtd html 4.0 transitional//en">
+<!--
+ 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.
+-->
+<html>
+<head>
+ <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
+</head>
+<body>
+<p>API and code to convert text into indexable/searchable tokens. Covers {@link org.apache.lucene.analysis.Analyzer} and related classes.</p>
+<h2>Parsing? Tokenization? Analysis!</h2>
+<p>
+Lucene, indexing and search library, accepts only plain text input.
+<p>
+<h2>Parsing</h2>
+<p>
+Applications that build their search capabilities upon Lucene may support documents in various formats – HTML, XML, PDF, Word – just to name a few.
+Lucene does not care about the <i>Parsing</i> of these and other document formats, and it is the responsibility of the
+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.
+<p>
+<h2>Tokenization</h2>
+<p>
+Plain text passed to Lucene for indexing goes through a process generally called tokenization. Tokenization is the process
+of breaking input text into small indexing elements – tokens.
+The way input text is broken into tokens heavily influences how people will then be able to search for that text.
+For instance, sentences beginnings and endings can be identified to provide for more accurate phrase
+and proximity searches (though sentence identification is not provided by Lucene).
+<p>
+In some cases simply breaking the input text into tokens is not enough – a deeper <i>Analysis</i> may be needed.
+There are many post tokenization steps that can be done, including (but not limited to):
+<ul>
+ <li><a href="http://en.wikipedia.org/wiki/Stemming">Stemming</a> –
+ Replacing of words by their stems.
+ For instance with English stemming "bikes" is replaced by "bike";
+ now query "bike" can find both documents containing "bike" and those containing "bikes".
+ </li>
+ <li><a href="http://en.wikipedia.org/wiki/Stop_words">Stop Words Filtering</a> –
+ Common words like "the", "and" and "a" rarely add any value to a search.
+ Removing them shrinks the index size and increases performance.
+ It may also reduce some "noise" and actually improve search quality.
+ </li>
+ <li><a href="http://en.wikipedia.org/wiki/Text_normalization">Text Normalization</a> –
+ Stripping accents and other character markings can make for better searching.
+ </li>
+ <li><a href="http://en.wikipedia.org/wiki/Synonym">Synonym Expansion</a> –
+ Adding in synonyms at the same token position as the current word can mean better
+ matching when users search with words in the synonym set.
+ </li>
+</ul>
+<p>
+<h2>Core Analysis</h2>
+<p>
+ The analysis package provides the mechanism to convert Strings and Readers into tokens that can be indexed by Lucene. There
+ are three main classes in the package from which all analysis processes are derived. These are:
+ <ul>
+ <li>{@link org.apache.lucene.analysis.Analyzer} – An Analyzer is responsible for building a {@link org.apache.lucene.analysis.TokenStream} which can be consumed
+ by the indexing and searching processes. See below for more information on implementing your own Analyzer.</li>
+ <li>{@link org.apache.lucene.analysis.Tokenizer} – A Tokenizer is a {@link org.apache.lucene.analysis.TokenStream} and is responsible for breaking
+ up incoming text into tokens. In most cases, an Analyzer will use a Tokenizer as the first step in
+ the analysis process.</li>
+ <li>{@link org.apache.lucene.analysis.TokenFilter} – A TokenFilter is also a {@link org.apache.lucene.analysis.TokenStream} and is responsible
+ for modifying tokens that have been created by the Tokenizer. Common modifications performed by a
+ TokenFilter are: deletion, stemming, synonym injection, and down casing. Not all Analyzers require TokenFilters</li>
+ </ul>
+ <b>Lucene 2.9 introduces a new TokenStream API. Please see the section "New TokenStream API" below for more details.</b>
+</p>
+<h2>Hints, Tips and Traps</h2>
+<p>
+ The synergy between {@link org.apache.lucene.analysis.Analyzer} and {@link org.apache.lucene.analysis.Tokenizer}
+ is sometimes confusing. To ease on this confusion, some clarifications:
+ <ul>
+ <li>The {@link org.apache.lucene.analysis.Analyzer} is responsible for the entire task of
+ <u>creating</u> tokens out of the input text, while the {@link org.apache.lucene.analysis.Tokenizer}
+ is only responsible for <u>breaking</u> the input text into tokens. Very likely, tokens created
+ by the {@link org.apache.lucene.analysis.Tokenizer} would be modified or even omitted
+ by the {@link org.apache.lucene.analysis.Analyzer} (via one or more
+ {@link org.apache.lucene.analysis.TokenFilter}s) before being returned.
+ </li>
+ <li>{@link org.apache.lucene.analysis.Tokenizer} is a {@link org.apache.lucene.analysis.TokenStream},
+ but {@link org.apache.lucene.analysis.Analyzer} is not.
+ </li>
+ <li>{@link org.apache.lucene.analysis.Analyzer} is "field aware", but
+ {@link org.apache.lucene.analysis.Tokenizer} is not.
+ </li>
+ </ul>
+</p>
+<p>
+ Lucene Java provides a number of analysis capabilities, the most commonly used one being the {@link
+ org.apache.lucene.analysis.standard.StandardAnalyzer}. Many applications will have a long and industrious life with nothing more
+ than the StandardAnalyzer. However, there are a few other classes/packages that are worth mentioning:
+ <ol>
+ <li>{@link org.apache.lucene.analysis.PerFieldAnalyzerWrapper} – Most Analyzers perform the same operation on all
+ {@link org.apache.lucene.document.Field}s. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different
+ {@link org.apache.lucene.document.Field}s.</li>
+ <li>The contrib/analyzers library located at the root of the Lucene distribution has a number of different Analyzer implementations to solve a variety
+ of different problems related to searching. Many of the Analyzers are designed to analyze non-English languages.</li>
+ <li>The contrib/snowball library
+ located at the root of the Lucene distribution has Analyzer and TokenFilter
+ implementations for a variety of Snowball stemmers.
+ See <a href="http://snowball.tartarus.org">http://snowball.tartarus.org</a>
+ for more information on Snowball stemmers.</li>
+ <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>
+ </ol>
+</p>
+<p>
+ 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).
+ Perhaps your application would be just fine using the simple {@link org.apache.lucene.analysis.WhitespaceTokenizer} combined with a
+ {@link org.apache.lucene.analysis.StopFilter}. The contrib/benchmark library can be useful for testing out the speed of the analysis process.
+</p>
+<h2>Invoking the Analyzer</h2>
+<p>
+ Applications usually do not invoke analysis – Lucene does it for them:
+ <ul>
+ <li>At indexing, as a consequence of
+ {@link org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document) addDocument(doc)},
+ the Analyzer in effect for indexing is invoked for each indexed field of the added document.
+ </li>
+ <li>At search, as a consequence of
+ {@link org.apache.lucene.queryParser.QueryParser#parse(java.lang.String) QueryParser.parse(queryText)},
+ the QueryParser may invoke the Analyzer in effect.
+ Note that for some queries analysis does not take place, e.g. wildcard queries.
+ </li>
+ </ul>
+ However an application might invoke Analysis of any text for testing or for any other purpose, something like:
+ <PRE class="prettyprint">
+ Analyzer analyzer = new StandardAnalyzer(); // or any other analyzer
+ TokenStream ts = analyzer.tokenStream("myfield",new StringReader("some text goes here"));
+ while (ts.incrementToken()) {
+ System.out.println("token: "+ts));
+ }
+ </PRE>
+</p>
+<h2>Indexing Analysis vs. Search Analysis</h2>
+<p>
+ Selecting the "correct" analyzer is crucial
+ for search quality, and can also affect indexing and search performance.
+ The "correct" analyzer differs between applications.
+ Lucene java's wiki page
+ <a href="http://wiki.apache.org/lucene-java/AnalysisParalysis">AnalysisParalysis</a>
+ provides some data on "analyzing your analyzer".
+ Here are some rules of thumb:
+ <ol>
+ <li>Test test test... (did we say test?)</li>
+ <li>Beware of over analysis – might hurt indexing performance.</li>
+ <li>Start with same analyzer for indexing and search, otherwise searches would not find what they are supposed to...</li>
+ <li>In some cases a different analyzer is required for indexing and search, for instance:
+ <ul>
+ <li>Certain searches require more stop words to be filtered. (I.e. more than those that were filtered at indexing.)</li>
+ <li>Query expansion by synonyms, acronyms, auto spell correction, etc.</li>
+ </ul>
+ This might sometimes require a modified analyzer – see the next section on how to do that.
+ </li>
+ </ol>
+</p>
+<h2>Implementing your own Analyzer</h2>
+<p>Creating your own Analyzer is straightforward. It usually involves either wrapping an existing Tokenizer and set of TokenFilters to create a new Analyzer
+or creating both the Analyzer and a Tokenizer or TokenFilter. Before pursuing this approach, you may find it worthwhile
+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.
+If you are still committed to creating your own Analyzer or TokenStream derivation (Tokenizer or TokenFilter) have a look at
+the source code of any one of the many samples located in this package.
+</p>
+<p>
+ The following sections discuss some aspects of implementing your own analyzer.
+</p>
+<h3>Field Section Boundaries</h3>
+<p>
+ When {@link org.apache.lucene.document.Document#add(org.apache.lucene.document.Fieldable) document.add(field)}
+ is called multiple times for the same field name, we could say that each such call creates a new
+ section for that field in that document.
+ In fact, a separate call to
+ {@link org.apache.lucene.analysis.Analyzer#tokenStream(java.lang.String, java.io.Reader) tokenStream(field,reader)}
+ would take place for each of these so called "sections".
+ However, the default Analyzer behavior is to treat all these sections as one large section.
+ This allows phrase search and proximity search to seamlessly cross
+ boundaries between these "sections".
+ In other words, if a certain field "f" is added like this:
+ <PRE class="prettyprint">
+ document.add(new Field("f","first ends",...);
+ document.add(new Field("f","starts two",...);
+ indexWriter.addDocument(document);
+ </PRE>
+ Then, a phrase search for "ends starts" would find that document.
+ Where desired, this behavior can be modified by introducing a "position gap" between consecutive field "sections",
+ simply by overriding
+ {@link org.apache.lucene.analysis.Analyzer#getPositionIncrementGap(java.lang.String) Analyzer.getPositionIncrementGap(fieldName)}:
+ <PRE class="prettyprint">
+ Analyzer myAnalyzer = new StandardAnalyzer() {
+ public int getPositionIncrementGap(String fieldName) {
+ return 10;
+ }
+ };
+ </PRE>
+</p>
+<h3>Token Position Increments</h3>
+<p>
+ By default, all tokens created by Analyzers and Tokenizers have a
+ {@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute#getPositionIncrement() position increment} of one.
+ This means that the position stored for that token in the index would be one more than
+ that of the previous token.
+ Recall that phrase and proximity searches rely on position info.
+</p>
+<p>
+ If the selected analyzer filters the stop words "is" and "the", then for a document
+ containing the string "blue is the sky", only the tokens "blue", "sky" are indexed,
+ with position("sky") = 1 + position("blue"). Now, a phrase query "blue is the sky"
+ would find that document, because the same analyzer filters the same stop words from
+ that query. But also the phrase query "blue sky" would find that document.
+</p>
+<p>
+ If this behavior does not fit the application needs,
+ a modified analyzer can be used, that would increment further the positions of
+ tokens following a removed stop word, using
+ {@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute#setPositionIncrement(int)}.
+ This can be done with something like:
+ <PRE class="prettyprint">
+ public TokenStream tokenStream(final String fieldName, Reader reader) {
+ final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
+ TokenStream res = new TokenStream() {
+ TermAttribute termAtt = addAttribute(TermAttribute.class);
+ PositionIncrementAttribute posIncrAtt = addAttribute(PositionIncrementAttribute.class);
+
+ public boolean incrementToken() throws IOException {
+ int extraIncrement = 0;
+ while (true) {
+ boolean hasNext = ts.incrementToken();
+ if (hasNext) {
+ if (stopWords.contains(termAtt.term())) {
+ extraIncrement++; // filter this word
+ continue;
+ }
+ if (extraIncrement>0) {
+ posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
+ }
+ }
+ return hasNext;
+ }
+ }
+ };
+ return res;
+ }
+ </PRE>
+ Now, with this modified analyzer, the phrase query "blue sky" would find that document.
+ But note that this is yet not a perfect solution, because any phrase query "blue w1 w2 sky"
+ where both w1 and w2 are stop words would match that document.
+</p>
+<p>
+ Few more use cases for modifying position increments are:
+ <ol>
+ <li>Inhibiting phrase and proximity matches in sentence boundaries – for this, a tokenizer that
+ identifies a new sentence can add 1 to the position increment of the first token of the new sentence.</li>
+ <li>Injecting synonyms – here, synonyms of a token should be added after that token,
+ and their position increment should be set to 0.
+ As result, all synonyms of a token would be considered to appear in exactly the
+ same position as that token, and so would they be seen by phrase and proximity searches.</li>
+ </ol>
+</p>
+<h2>New TokenStream API</h2>
+<p>
+ With Lucene 2.9 we introduce a new TokenStream API. The old API used to produce Tokens. A Token
+ has getter and setter methods for different properties like positionIncrement and termText.
+ While this approach was sufficient for the default indexing format, it is not versatile enough for
+ Flexible Indexing, a term which summarizes the effort of making the Lucene indexer pluggable and extensible for custom
+ index formats.
+</p>
+<p>
+A fully customizable indexer means that users will be able to store custom data structures on disk. Therefore an API
+is necessary that can transport custom types of data from the documents to the indexer.
+</p>
+<h3>Attribute and AttributeSource</h3>
+Lucene 2.9 therefore introduces a new pair of classes called {@link org.apache.lucene.util.Attribute} and
+{@link org.apache.lucene.util.AttributeSource}. An Attribute serves as a
+particular piece of information about a text token. For example, {@link org.apache.lucene.analysis.tokenattributes.TermAttribute}
+ contains the term text of a token, and {@link org.apache.lucene.analysis.tokenattributes.OffsetAttribute} contains the start and end character offsets of a token.
+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
+means that one can add Attributes to a TokenStream. Since TokenFilter extends TokenStream, all filters are also
+AttributeSources.
+<p>
+ Lucene now provides six Attributes out of the box, which replace the variables the Token class has:
+ <ul>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.TermAttribute}<p>The term text of a token.</p></li>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.OffsetAttribute}<p>The start and end offset of token in characters.</p></li>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute}<p>See above for detailed information about position increment.</p></li>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.PayloadAttribute}<p>The payload that a Token can optionally have.</p></li>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.TypeAttribute}<p>The type of the token. Default is 'word'.</p></li>
+ <li>{@link org.apache.lucene.analysis.tokenattributes.FlagsAttribute}<p>Optional flags a token can have.</p></li>
+ </ul>
+</p>
+<h3>Using the new TokenStream API</h3>
+There are a few important things to know in order to use the new API efficiently which are summarized here. You may want
+to walk through the example below first and come back to this section afterwards.
+<ol><li>
+Please keep in mind that an AttributeSource can only have one instance of a particular Attribute. Furthermore, if
+a chain of a TokenStream and multiple TokenFilters is used, then all TokenFilters in that chain share the Attributes
+with the TokenStream.
+</li>
+<br>
+<li>
+Attribute instances are reused for all tokens of a document. Thus, a TokenStream/-Filter needs to update
+the appropriate Attribute(s) in incrementToken(). The consumer, commonly the Lucene indexer, consumes the data in the
+Attributes and then calls incrementToken() again until it returns false, which indicates that the end of the stream
+was reached. This means that in each call of incrementToken() a TokenStream/-Filter can safely overwrite the data in
+the Attribute instances.
+</li>
+<br>
+<li>
+For performance reasons a TokenStream/-Filter should add/get Attributes during instantiation; i.e., create an attribute in the
+constructor and store references to it in an instance variable. Using an instance variable instead of calling addAttribute()/getAttribute()
+in incrementToken() will avoid attribute lookups for every token in the document.
+</li>
+<br>
+<li>
+All methods in AttributeSource are idempotent, which means calling them multiple times always yields the same
+result. This is especially important to know for addAttribute(). The method takes the <b>type</b> (<code>Class</code>)
+of an Attribute as an argument and returns an <b>instance</b>. If an Attribute of the same type was previously added, then
+the already existing instance is returned, otherwise a new instance is created and returned. Therefore TokenStreams/-Filters
+can safely call addAttribute() with the same Attribute type multiple times. Even consumers of TokenStreams should
+normally call addAttribute() instead of getAttribute(), because it would not fail if the TokenStream does not have this
+Attribute (getAttribute() would throw an IllegalArgumentException, if the Attribute is missing). More advanced code
+could simply check with hasAttribute(), if a TokenStream has it, and may conditionally leave out processing for
+extra performance.
+</li></ol>
+<h3>Example</h3>
+In this example we will create a WhiteSpaceTokenizer and use a LengthFilter to suppress all words that only
+have two or less characters. The LengthFilter is part of the Lucene core and its implementation will be explained
+here to illustrate the usage of the new TokenStream API.<br>
+Then we will develop a custom Attribute, a PartOfSpeechAttribute, and add another filter to the chain which
+utilizes the new custom attribute, and call it PartOfSpeechTaggingFilter.
+<h4>Whitespace tokenization</h4>
+<pre class="prettyprint">
+public class MyAnalyzer extends Analyzer {
+
+ public TokenStream tokenStream(String fieldName, Reader reader) {
+ TokenStream stream = new WhitespaceTokenizer(reader);
+ return stream;
+ }
+
+ public static void main(String[] args) throws IOException {
+ // text to tokenize
+ final String text = "This is a demo of the new TokenStream API";
+
+ MyAnalyzer analyzer = new MyAnalyzer();
+ TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
+
+ // get the TermAttribute from the TokenStream
+ TermAttribute termAtt = stream.addAttribute(TermAttribute.class);
+
+ stream.reset();
+
+ // print all tokens until stream is exhausted
+ while (stream.incrementToken()) {
+ System.out.println(termAtt.term());
+ }
+
+ stream.end()
+ stream.close();
+ }
+}
+</pre>
+In this easy example a simple white space tokenization is performed. In main() a loop consumes the stream and
+prints the term text of the tokens by accessing the TermAttribute that the WhitespaceTokenizer provides.
+Here is the output:
+<pre>
+This
+is
+a
+demo
+of
+the
+new
+TokenStream
+API
+</pre>
+<h4>Adding a LengthFilter</h4>
+We want to suppress all tokens that have 2 or less characters. We can do that easily by adding a LengthFilter
+to the chain. Only the tokenStream() method in our analyzer needs to be changed:
+<pre class="prettyprint">
+ public TokenStream tokenStream(String fieldName, Reader reader) {
+ TokenStream stream = new WhitespaceTokenizer(reader);
+ stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
+ return stream;
+ }
+</pre>
+Note how now only words with 3 or more characters are contained in the output:
+<pre>
+This
+demo
+the
+new
+TokenStream
+API
+</pre>
+Now let's take a look how the LengthFilter is implemented (it is part of Lucene's core):
+<pre class="prettyprint">
+public final class LengthFilter extends TokenFilter {
+
+ final int min;
+ final int max;
+
+ private TermAttribute termAtt;
+
+ /**
+ * Build a filter that removes words that are too long or too
+ * short from the text.
+ */
+ public LengthFilter(TokenStream in, int min, int max)
+ {
+ super(in);
+ this.min = min;
+ this.max = max;
+ termAtt = addAttribute(TermAttribute.class);
+ }
+
+ /**
+ * Returns the next input Token whose term() is the right len
+ */
+ public final boolean incrementToken() throws IOException
+ {
+ assert termAtt != null;
+ // return the first non-stop word found
+ while (input.incrementToken()) {
+ int len = termAtt.termLength();
+ if (len >= min && len <= max) {
+ return true;
+ }
+ // note: else we ignore it but should we index each part of it?
+ }
+ // reached EOS -- return null
+ return false;
+ }
+}
+</pre>
+The TermAttribute is added in the constructor and stored in the instance variable <code>termAtt</code>.
+Remember that there can only be a single instance of TermAttribute in the chain, so in our example the
+<code>addAttribute()</code> call in LengthFilter returns the TermAttribute that the WhitespaceTokenizer already added. The tokens
+are retrieved from the input stream in the <code>incrementToken()</code> method. By looking at the term text
+in the TermAttribute the length of the term can be determined and too short or too long tokens are skipped.
+Note how <code>incrementToken()</code> can efficiently access the instance variable; no attribute lookup
+is neccessary. The same is true for the consumer, which can simply use local references to the Attributes.
+
+<h4>Adding a custom Attribute</h4>
+Now we're going to implement our own custom Attribute for part-of-speech tagging and call it consequently
+<code>PartOfSpeechAttribute</code>. First we need to define the interface of the new Attribute:
+<pre class="prettyprint">
+ public interface PartOfSpeechAttribute extends Attribute {
+ public static enum PartOfSpeech {
+ Noun, Verb, Adjective, Adverb, Pronoun, Preposition, Conjunction, Article, Unknown
+ }
+
+ public void setPartOfSpeech(PartOfSpeech pos);
+
+ public PartOfSpeech getPartOfSpeech();
+ }
+</pre>
+
+Now we also need to write the implementing class. The name of that class is important here: By default, Lucene
+checks if there is a class with the name of the Attribute with the postfix 'Impl'. In this example, we would
+consequently call the implementing class <code>PartOfSpeechAttributeImpl</code>. <br/>
+This should be the usual behavior. However, there is also an expert-API that allows changing these naming conventions:
+{@link org.apache.lucene.util.AttributeSource.AttributeFactory}. The factory accepts an Attribute interface as argument
+and returns an actual instance. You can implement your own factory if you need to change the default behavior. <br/><br/>
+
+Now here is the actual class that implements our new Attribute. Notice that the class has to extend
+{@link org.apache.lucene.util.AttributeImpl}:
+
+<pre class="prettyprint">
+public final class PartOfSpeechAttributeImpl extends AttributeImpl
+ implements PartOfSpeechAttribute{
+
+ private PartOfSpeech pos = PartOfSpeech.Unknown;
+
+ public void setPartOfSpeech(PartOfSpeech pos) {
+ this.pos = pos;
+ }
+
+ public PartOfSpeech getPartOfSpeech() {
+ return pos;
+ }
+
+ public void clear() {
+ pos = PartOfSpeech.Unknown;
+ }
+
+ public void copyTo(AttributeImpl target) {
+ ((PartOfSpeechAttributeImpl) target).pos = pos;
+ }
+
+ public boolean equals(Object other) {
+ if (other == this) {
+ return true;
+ }
+
+ if (other instanceof PartOfSpeechAttributeImpl) {
+ return pos == ((PartOfSpeechAttributeImpl) other).pos;
+ }
+
+ return false;
+ }
+
+ public int hashCode() {
+ return pos.ordinal();
+ }
+}
+</pre>
+This is a simple Attribute implementation has only a single variable that stores the part-of-speech of a token. It extends the
+new <code>AttributeImpl</code> class and therefore implements its abstract methods <code>clear(), copyTo(), equals(), hashCode()</code>.
+Now we need a TokenFilter that can set this new PartOfSpeechAttribute for each token. In this example we show a very naive filter
+that tags every word with a leading upper-case letter as a 'Noun' and all other words as 'Unknown'.
+<pre class="prettyprint">
+ public static class PartOfSpeechTaggingFilter extends TokenFilter {
+ PartOfSpeechAttribute posAtt;
+ TermAttribute termAtt;
+
+ protected PartOfSpeechTaggingFilter(TokenStream input) {
+ super(input);
+ posAtt = addAttribute(PartOfSpeechAttribute.class);
+ termAtt = addAttribute(TermAttribute.class);
+ }
+
+ public boolean incrementToken() throws IOException {
+ if (!input.incrementToken()) {return false;}
+ posAtt.setPartOfSpeech(determinePOS(termAtt.termBuffer(), 0, termAtt.termLength()));
+ return true;
+ }
+
+ // determine the part of speech for the given term
+ protected PartOfSpeech determinePOS(char[] term, int offset, int length) {
+ // naive implementation that tags every uppercased word as noun
+ if (length > 0 && Character.isUpperCase(term[0])) {
+ return PartOfSpeech.Noun;
+ }
+ return PartOfSpeech.Unknown;
+ }
+ }
+</pre>
+Just like the LengthFilter, this new filter accesses the attributes it needs in the constructor and
+stores references in instance variables. Notice how you only need to pass in the interface of the new
+Attribute and instantiating the correct class is automatically been taken care of.
+Now we need to add the filter to the chain:
+<pre class="prettyprint">
+ public TokenStream tokenStream(String fieldName, Reader reader) {
+ TokenStream stream = new WhitespaceTokenizer(reader);
+ stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
+ stream = new PartOfSpeechTaggingFilter(stream);
+ return stream;
+ }
+</pre>
+Now let's look at the output:
+<pre>
+This
+demo
+the
+new
+TokenStream
+API
+</pre>
+Apparently it hasn't changed, which shows that adding a custom attribute to a TokenStream/Filter chain does not
+affect any existing consumers, simply because they don't know the new Attribute. Now let's change the consumer
+to make use of the new PartOfSpeechAttribute and print it out:
+<pre class="prettyprint">
+ public static void main(String[] args) throws IOException {
+ // text to tokenize
+ final String text = "This is a demo of the new TokenStream API";
+
+ MyAnalyzer analyzer = new MyAnalyzer();
+ TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
+
+ // get the TermAttribute from the TokenStream
+ TermAttribute termAtt = stream.addAttribute(TermAttribute.class);
+
+ // get the PartOfSpeechAttribute from the TokenStream
+ PartOfSpeechAttribute posAtt = stream.addAttribute(PartOfSpeechAttribute.class);
+
+ stream.reset();
+
+ // print all tokens until stream is exhausted
+ while (stream.incrementToken()) {
+ System.out.println(termAtt.term() + ": " + posAtt.getPartOfSpeech());
+ }
+
+ stream.end();
+ stream.close();
+ }
+</pre>
+The change that was made is to get the PartOfSpeechAttribute from the TokenStream and print out its contents in
+the while loop that consumes the stream. Here is the new output:
+<pre>
+This: Noun
+demo: Unknown
+the: Unknown
+new: Unknown
+TokenStream: Noun
+API: Noun
+</pre>
+Each word is now followed by its assigned PartOfSpeech tag. Of course this is a naive
+part-of-speech tagging. The word 'This' should not even be tagged as noun; it is only spelled capitalized because it
+is the first word of a sentence. Actually this is a good opportunity for an excerise. To practice the usage of the new
+API the reader could now write an Attribute and TokenFilter that can specify for each word if it was the first token
+of a sentence or not. Then the PartOfSpeechTaggingFilter can make use of this knowledge and only tag capitalized words
+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).
+As a small hint, this is how the new Attribute class could begin:
+<pre class="prettyprint">
+ public class FirstTokenOfSentenceAttributeImpl extends Attribute
+ implements FirstTokenOfSentenceAttribute {
+
+ private boolean firstToken;
+
+ public void setFirstToken(boolean firstToken) {
+ this.firstToken = firstToken;
+ }
+
+ public boolean getFirstToken() {
+ return firstToken;
+ }
+
+ public void clear() {
+ firstToken = false;
+ }
+
+ ...
+</pre>
+</body>
+</html>
fnp / pylucene.git / blobdiff