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. -
-
-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 Parsing of these and other document formats, and it is the responsibility of the -application using Lucene to use an appropriate Parser to convert the original format into plain text before passing that plain text to Lucene. -
-
-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). -
-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): -
-
- 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 {@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: -
- 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. -
-- 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));
- }
-
-
-- 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 - AnalysisParalysis - provides some data on "analyzing your analyzer". - Here are some rules of thumb: -
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. -
-- The following sections discuss some aspects of implementing your own analyzer. -
-- 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: -
- 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;
- }
- };
-
-
-- 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. -
-- 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. -
-- 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() {
- 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;
- }
-
- 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. -
--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 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.
Class)
-of an Attribute as an argument and returns an instance. 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.
-
-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();
- }
-}
-
-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:
--This -is -a -demo -of -the -new -TokenStream -API --
- public TokenStream tokenStream(String fieldName, Reader reader) {
- TokenStream stream = new WhitespaceTokenizer(reader);
- stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
- return stream;
- }
-
-Note how now only words with 3 or more characters are contained in the output:
--This -demo -the -new -TokenStream -API --Now let's take a look how the LengthFilter is implemented (it is part of Lucene's core): -
-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;
- }
-}
-
-The TermAttribute is added in the constructor and stored in the instance variable termAtt.
-Remember that there can only be a single instance of TermAttribute 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 TermAttribute 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.
-
-PartOfSpeechAttribute. First we need to define the interface of the new Attribute:
-
- 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();
- }
-
-
-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 PartOfSpeechAttributeImpl.
-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();
- }
-}
-
-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'.
-
- 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;
- }
- }
-
-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:
-
- 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;
- }
-
-Now let's look at the output:
--This -demo -the -new -TokenStream -API --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: -
- 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();
- }
-
-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:
--This: Noun -demo: Unknown -the: Unknown -new: Unknown -TokenStream: Noun -API: Noun --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: -
- 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;
- }
-
- ...
-
-
-