API and code to convert text into indexable/searchable tokens. Covers {@link org.apache.lucene.analysis.Analyzer} and related classes.
-Lucene, indexing and search library, accepts only plain text input. +Lucene, an indexing and search library, accepts only plain text input.
@@ -39,12 +39,23 @@ The way input text is broken into tokens heavily influences how people will then 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).
-In some cases simply breaking the input text into tokens is not enough – a deeper Analysis may be needed. -There are many post tokenization steps that can be done, including (but not limited to): + In some cases simply breaking the input text into tokens is not enough + – a deeper Analysis may be needed. Lucene includes both + pre- and post-tokenization analysis facilities. +
++ Pre-tokenization analysis can include (but is not limited to) stripping + HTML markup, and transforming or removing text matching arbitrary patterns + or sets of fixed strings. +
++ There are many post-tokenization steps that can be done, including + (but not limited to): +
- 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: -
- 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: -
Lucene Java provides a number of analysis capabilities, the most commonly used one being the 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: -
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 WhitespaceTokenizer combined with a StopFilter. The contrib/benchmark library can be useful @@ -118,24 +164,28 @@ There are many post tokenization steps that can be done, including (but not limi
Applications usually do not invoke analysis – Lucene does it for them: -
- 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));
- }
-
++ However an application might invoke Analysis of any text for testing or for any other purpose, something like: +
+
+ Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_XY); // or any other analyzer
+ TokenStream ts = analyzer.tokenStream("myfield",new StringReader("some text goes here"));
+ while (ts.incrementToken()) {
+ System.out.println("token: "+ts));
+ }
+
Selecting the "correct" analyzer is crucial @@ -159,11 +209,18 @@ There are many post tokenization steps that can be done, including (but not limi
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 modules/analysis 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. +
+ Creating your own Analyzer is straightforward. Your Analyzer can wrap + existing analysis components — CharFilter(s) (optional), a + Tokenizer, and TokenFilter(s) (optional) — or components you + create, or a combination of existing and newly created components. 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, have a look at the source code of any one of the many samples + located in this package.
The following sections discuss some aspects of implementing your own analyzer. @@ -180,23 +237,25 @@ the source code of any one of the many samples located in this package. 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: -
- document.add(new Field("f","first ends",...);
- document.add(new Field("f","starts two",...);
- indexWriter.addDocument(document);
-
+
+
+ document.add(new Field("f","first ends",...);
+ document.add(new Field("f","starts two",...);
+ indexWriter.addDocument(document);
+
+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)}: -
- Analyzer myAnalyzer = new StandardAnalyzer() {
- public int getPositionIncrementGap(String fieldName) {
- return 10;
- }
- };
-
+
+ Analyzer myAnalyzer = new StandardAnalyzer() {
+ public int getPositionIncrementGap(String fieldName) {
+ return 10;
+ }
+ };
+
By default, all tokens created by Analyzers and Tokenizers have a @@ -213,85 +272,122 @@ the source code of any one of the many samples located in this package. that query. But also the phrase query "blue sky" would find that document.
- 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 + 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: -
- public TokenStream tokenStream(final String fieldName, Reader reader) {
- final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
- TokenStream res = new TokenStream() {
- CharTermAttribute termAtt = addAttribute(CharTermAttribute.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.toString())) {
- extraIncrement++; // filter this word
- continue;
- }
- if (extraIncrement>0) {
- posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
- }
- }
- return hasNext;
+ This can be done with something like the following (note, however, that
+ StopFilter natively includes this capability by subclassing
+ FilteringTokenFilter}:
+
+
+ public TokenStream tokenStream(final String fieldName, Reader reader) {
+ final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
+ TokenStream res = new TokenStream() {
+ CharTermAttribute termAtt = addAttribute(CharTermAttribute.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.toString())) {
+ extraIncrement++; // filter this word
+ continue;
+ }
+ if (extraIncrement>0) {
+ posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
}
}
- };
- return res;
+ return hasNext;
+ }
}
-
- 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.
+ };
+ return res;
+ }
+
++ 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.
- Few more use cases for modifying position increments are: -
- 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. + "Flexible Indexing" summarizes the effort of making the Lucene indexer + pluggable and extensible for custom index formats. 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. +
++ Classes {@link org.apache.lucene.util.Attribute} and + {@link org.apache.lucene.util.AttributeSource} serve as the basis upon which + the analysis elements of "Flexible Indexing" are implemented. An Attribute + holds a particular piece of information about a text token. For example, + {@link org.apache.lucene.analysis.tokenattributes.CharTermAttribute} + 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.
-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. + Lucene provides seven Attributes out of the box:
-- Lucene now provides six Attributes out of the box, which replace the variables the Token class has: -
The term text of a token.
The start and end offset of token in characters.
See above for detailed information about position increment.
The payload that a Token can optionally have.
The type of the token. Default is 'word'.
Optional flags a token can have.
| {@link org.apache.lucene.analysis.tokenattributes.CharTermAttribute} | ++ The term text of a token. Implements {@link java.lang.CharSequence} + (providing methods length() and charAt(), and allowing e.g. for direct + use with regular expression {@link java.util.regex.Matcher}s) and + {@link java.lang.Appendable} (allowing the term text to be appended to.) + | +
| {@link org.apache.lucene.analysis.tokenattributes.OffsetAttribute} | +The start and end offset of a token in characters. | +
| {@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute} | +See above for detailed information about position increment. | +
| {@link org.apache.lucene.analysis.tokenattributes.PayloadAttribute} | +The payload that a Token can optionally have. | +
| {@link org.apache.lucene.analysis.tokenattributes.TypeAttribute} | +The type of the token. Default is 'word'. | +
| {@link org.apache.lucene.analysis.tokenattributes.FlagsAttribute} | +Optional flags a token can have. | +
| {@link org.apache.lucene.analysis.tokenattributes.KeywordAttribute} | ++ Keyword-aware TokenStreams/-Filters skip modification of tokens that + return true from this attribute's isKeyword() method. + | +
+ In this example we will create a WhiteSpaceTokenizer and use a LengthFilter to suppress all words that have + only two or fewer characters. The LengthFilter is part of the Lucene core and its implementation will be explained + here to illustrate the usage of the TokenStream API. +
++ 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. +
public class MyAnalyzer extends Analyzer {
- public TokenStream tokenStream(String fieldName, Reader reader) {
- TokenStream stream = new WhitespaceTokenizer(reader);
- return stream;
+ private Version matchVersion;
+
+ public MyAnalyzer(Version matchVersion) {
+ this.matchVersion = matchVersion;
+ }
+
+ {@literal @Override}
+ protected TokenStreamComponents createComponents(String fieldName, Reader reader) {
+ return new TokenStreamComponents(new WhitespaceTokenizer(matchVersion, reader));
}
public static void main(String[] args) throws IOException {
// text to tokenize
- final String text = "This is a demo of the new TokenStream API";
+ final String text = "This is a demo of the TokenStream API";
- MyAnalyzer analyzer = new MyAnalyzer();
+ Version matchVersion = Version.LUCENE_XY; // Substitute desired Lucene version for XY
+ MyAnalyzer analyzer = new MyAnalyzer(matchVersion);
TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
// get the CharTermAttribute from the TokenStream
@@ -377,13 +484,15 @@ TokenStream
API
createComponents() method in our analyzer needs to be changed:
- public TokenStream tokenStream(String fieldName, Reader reader) {
- TokenStream stream = new WhitespaceTokenizer(reader);
- stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
- return stream;
+ {@literal @Override}
+ protected TokenStreamComponents createComponents(String fieldName, Reader reader) {
+ final Tokenizer source = new WhitespaceTokenizer(matchVersion, reader);
+ TokenStream result = new LengthFilter(source, 3, Integer.MAX_VALUE);
+ return new TokenStreamComponents(source, result);
}
Note how now only words with 3 or more characters are contained in the output:
@@ -395,53 +504,119 @@ new
TokenStream
API
-Now let's take a look how the LengthFilter is implemented (it is part of Lucene's core):
+Now let's take a look how the LengthFilter is implemented:
-public final class LengthFilter extends TokenFilter {
+public final class LengthFilter extends FilteringTokenFilter {
- final int min;
- final int max;
+ private final int min;
+ private final int max;
- private CharTermAttribute termAtt;
+ private final CharTermAttribute termAtt = addAttribute(CharTermAttribute.class);
/**
* 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);
+ public LengthFilter(boolean enablePositionIncrements, TokenStream in, int min, int max) {
+ super(enablePositionIncrements, in);
this.min = min;
this.max = max;
- termAtt = addAttribute(CharTermAttribute.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.length();
- 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;
+ {@literal @Override}
+ public boolean accept() throws IOException {
+ final int len = termAtt.length();
+ return (len >= min && len <= max);
+ }
+}
+
+
+ In LengthFilter, the CharTermAttribute is added and stored in the instance
+ variable termAtt. Remember that there can only be a single
+ instance of CharTermAttribute in the chain, so in our example the
+ addAttribute() call in LengthFilter returns the
+ CharTermAttribute that the WhitespaceTokenizer already added.
+
+ The tokens are retrieved from the input stream in FilteringTokenFilter's
+ incrementToken() method (see below), which calls LengthFilter's
+ accept() method. By looking at the term text in the
+ CharTermAttribute, the length of the term can be determined and tokens that
+ are either too short or too long are skipped. Note how
+ accept() 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.
+
+ LengthFilter extends FilteringTokenFilter: +
+ +
+public abstract class FilteringTokenFilter extends TokenFilter {
+
+ private final PositionIncrementAttribute posIncrAtt = addAttribute(PositionIncrementAttribute.class);
+ private boolean enablePositionIncrements; // no init needed, as ctor enforces setting value!
+
+ public FilteringTokenFilter(boolean enablePositionIncrements, TokenStream input){
+ super(input);
+ this.enablePositionIncrements = enablePositionIncrements;
+ }
+
+ /** Override this method and return if the current input token should be returned by {@literal {@link #incrementToken}}. */
+ protected abstract boolean accept() throws IOException;
+
+ {@literal @Override}
+ public final boolean incrementToken() throws IOException {
+ if (enablePositionIncrements) {
+ int skippedPositions = 0;
+ while (input.incrementToken()) {
+ if (accept()) {
+ if (skippedPositions != 0) {
+ posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement() + skippedPositions);
+ }
+ return true;
+ }
+ skippedPositions += posIncrAtt.getPositionIncrement();
+ }
+ } else {
+ while (input.incrementToken()) {
+ if (accept()) {
+ return true;
+ }
+ }
+ }
+ // reached EOS -- return false
+ return false;
+ }
+
+ /**
+ * {@literal @see #setEnablePositionIncrements(boolean)}
+ */
+ public boolean getEnablePositionIncrements() {
+ return enablePositionIncrements;
+ }
+
+ /**
+ * If true, this TokenFilter will preserve
+ * positions of the incoming tokens (ie, accumulate and
+ * set position increments of the removed tokens).
+ * Generally, true is best as it does not
+ * lose information (positions of the original tokens)
+ * during indexing.
+ *
+ * When set, when a token is stopped
+ * (omitted), the position increment of the following
+ * token is incremented.
+ *
+ *
NOTE: be sure to also
+ * set org.apache.lucene.queryparser.classic.QueryParser#setEnablePositionIncrements if
+ * you use QueryParser to create queries.
+ */
+ public void setEnablePositionIncrements(boolean enable) {
+ this.enablePositionIncrements = enable;
}
}
-The CharTermAttribute is added in the constructor and stored in the instance variable termAtt.
-Remember that there can only be a single instance of CharTermAttribute in the chain, so in our example the
-addAttribute() call in LengthFilter returns the TermAttribute that the WhitespaceTokenizer already added. The tokens
-are retrieved from the input stream in the incrementToken() method. By looking at the term text
-in the CharTermAttribute the length of the term can be determined and too short or too long tokens are skipped.
-Note how incrementToken() 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.
PartOfSpeechAttributeImpl.
+ 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 suffix 'Impl'. In this example, we would
+ consequently call the implementing class PartOfSpeechAttributeImpl.
+
+ 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. +
++ Now here is the actual class that implements our new Attribute. Notice that the class has to extend + {@link org.apache.lucene.util.AttributeImpl}: +
public final class PartOfSpeechAttributeImpl extends AttributeImpl
- implements PartOfSpeechAttribute{
+ implements PartOfSpeechAttribute {
private PartOfSpeech pos = PartOfSpeech.Unknown;
@@ -482,44 +660,33 @@ public final class PartOfSpeechAttributeImpl extends AttributeImpl
return pos;
}
+ {@literal @Override}
public void clear() {
pos = PartOfSpeech.Unknown;
}
+ {@literal @Override}
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();
+ ((PartOfSpeechAttribute) target).pos = pos;
}
}
-This is a simple Attribute implementation has only a single variable that stores the part-of-speech of a token. It extends the
-new AttributeImpl class and therefore implements its abstract methods clear(), copyTo(), equals(), hashCode().
-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'.
+
+ This is a simple Attribute implementation has only a single variable that
+ stores the part-of-speech of a token. It extends the
+ AttributeImpl class and therefore implements its abstract methods
+ clear() and copyTo(). 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'.
+
public static class PartOfSpeechTaggingFilter extends TokenFilter {
- PartOfSpeechAttribute posAtt;
- CharTermAttribute termAtt;
+ PartOfSpeechAttribute posAtt = addAttribute(PartOfSpeechAttribute.class);
+ CharTermAttribute termAtt = addAttribute(CharTermAttribute.class);
protected PartOfSpeechTaggingFilter(TokenStream input) {
super(input);
- posAtt = addAttribute(PartOfSpeechAttribute.class);
- termAtt = addAttribute(CharTermAttribute.class);
}
public boolean incrementToken() throws IOException {
@@ -538,16 +705,20 @@ that tags every word with a leading upper-case letter as a 'Noun' and all other
}
}
-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:
++ Just like the LengthFilter, this new filter stores references to the + attributes it needs in instance variables. Notice how you only need to pass + in the interface of the new Attribute and instantiating the correct class + is automatically taken care of. +
+Now we need to add the filter to the chain in MyAnalyzer:
- 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;
+ {@literal @Override}
+ protected TokenStreamComponents createComponents(String fieldName, Reader reader) {
+ final Tokenizer source = new WhitespaceTokenizer(matchVersion, reader);
+ TokenStream result = new LengthFilter(source, 3, Integer.MAX_VALUE);
+ result = new PartOfSpeechTaggingFilter(result);
+ return new TokenStreamComponents(source, result);
}
Now let's look at the output:
@@ -565,7 +736,7 @@ to make use of the new PartOfSpeechAttribute and print it out:
public static void main(String[] args) throws IOException {
// text to tokenize
- final String text = "This is a demo of the new TokenStream API";
+ final String text = "This is a demo of the TokenStream API";
MyAnalyzer analyzer = new MyAnalyzer();
TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
@@ -605,8 +776,8 @@ of a sentence or not. Then the PartOfSpeechTaggingFilter can make use of this kn
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:
- public class FirstTokenOfSentenceAttributeImpl extends Attribute
- implements FirstTokenOfSentenceAttribute {
+ public class FirstTokenOfSentenceAttributeImpl extends AttributeImpl
+ implements FirstTokenOfSentenceAttribute {
private boolean firstToken;
@@ -618,6 +789,7 @@ As a small hint, this is how the new Attribute class could begin:
return firstToken;
}
+ {@literal @Override}
public void clear() {
firstToken = false;
}