See: Description
Class | Description |
---|---|
Analyzer |
An Analyzer builds TokenStreams, which analyze text.
|
ASCIIFoldingFilter |
This class converts alphabetic, numeric, and symbolic Unicode characters
which are not in the first 127 ASCII characters (the "Basic Latin" Unicode
block) into their ASCII equivalents, if one exists.
|
BaseCharFilter |
Base utility class for implementing a
CharFilter . |
CachingTokenFilter |
This class can be used if the token attributes of a TokenStream
are intended to be consumed more than once.
|
CharArrayMap<V> |
A simple class that stores key Strings as char[]'s in a
hash table.
|
CharArraySet |
A simple class that stores Strings as char[]'s in a
hash table.
|
CharFilter |
Subclasses of CharFilter can be chained to filter CharStream.
|
CharReader |
CharReader is a Reader wrapper.
|
CharStream |
CharStream adds
CharStream.correctOffset(int)
functionality over Reader . |
CharTokenizer |
An abstract base class for simple, character-oriented tokenizers.
|
FilteringTokenFilter |
Abstract base class for TokenFilters that may remove tokens.
|
ISOLatin1AccentFilter | Deprecated
If you build a new index, use
ASCIIFoldingFilter
which covers a superset of Latin 1. |
KeywordAnalyzer |
"Tokenizes" the entire stream as a single token.
|
KeywordMarkerFilter |
Marks terms as keywords via the
KeywordAttribute . |
KeywordTokenizer |
Emits the entire input as a single token.
|
LengthFilter |
Removes words that are too long or too short from the stream.
|
LetterTokenizer |
A LetterTokenizer is a tokenizer that divides text at non-letters.
|
LimitTokenCountAnalyzer |
This Analyzer limits the number of tokens while indexing.
|
LimitTokenCountFilter |
This TokenFilter limits the number of tokens while indexing.
|
LowerCaseFilter |
Normalizes token text to lower case.
|
LowerCaseTokenizer |
LowerCaseTokenizer performs the function of LetterTokenizer
and LowerCaseFilter together.
|
MappingCharFilter |
Simplistic
CharFilter that applies the mappings
contained in a NormalizeCharMap to the character
stream, and correcting the resulting changes to the
offsets. |
NormalizeCharMap |
Holds a map of String input to String output, to be used
with
MappingCharFilter . |
NumericTokenStream |
Expert: This class provides a
TokenStream
for indexing numeric values that can be used by NumericRangeQuery or NumericRangeFilter . |
PerFieldAnalyzerWrapper |
This analyzer is used to facilitate scenarios where different
fields require different analysis techniques.
|
PorterStemFilter |
Transforms the token stream as per the Porter stemming algorithm.
|
ReusableAnalyzerBase |
An convenience subclass of Analyzer that makes it easy to implement
TokenStream reuse. |
ReusableAnalyzerBase.TokenStreamComponents |
This class encapsulates the outer components of a token stream.
|
SimpleAnalyzer |
An
Analyzer that filters LetterTokenizer
with LowerCaseFilter
You must specify the required Version compatibility
when creating CharTokenizer :
As of 3.1, LowerCaseTokenizer uses an int based API to normalize and
detect token codepoints. |
StopAnalyzer | |
StopFilter |
Removes stop words from a token stream.
|
StopwordAnalyzerBase |
Base class for Analyzers that need to make use of stopword sets.
|
TeeSinkTokenFilter |
This TokenFilter provides the ability to set aside attribute states
that have already been analyzed.
|
TeeSinkTokenFilter.SinkFilter |
A filter that decides which
AttributeSource states to store in the sink. |
TeeSinkTokenFilter.SinkTokenStream |
TokenStream output from a tee with optional filtering.
|
Token |
A Token is an occurrence of a term from the text of a field.
|
Token.TokenAttributeFactory |
Expert: Creates a TokenAttributeFactory returning
Token as instance for the basic attributes
and for all other attributes calls the given delegate factory. |
TokenFilter |
A TokenFilter is a TokenStream whose input is another TokenStream.
|
Tokenizer |
A Tokenizer is a TokenStream whose input is a Reader.
|
TokenStream | |
TypeTokenFilter |
Removes tokens whose types appear in a set of blocked types from a token stream.
|
WhitespaceAnalyzer |
An Analyzer that uses
WhitespaceTokenizer . |
WhitespaceTokenizer |
A WhitespaceTokenizer is a tokenizer that divides text at whitespace.
|
WordlistLoader |
Loader for text files that represent a list of stopwords.
|
API and code to convert text into indexable/searchable tokens. Covers Analyzer
and related classes.
Lucene, an 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. 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 four main classes in the package from which all analysis processes are derived. These are:
Analyzer
– An Analyzer is
responsible for building a
TokenStream
which can be consumed
by the indexing and searching processes. See below for more information
on implementing your own Analyzer.
CharFilter
– CharFilter extends
Reader
to perform pre-tokenization substitutions,
deletions, and/or insertions on an input Reader's text, while providing
corrected character offsets to account for these modifications. This
capability allows highlighting to function over the original text when
indexed tokens are created from CharFilter-modified text with offsets
that are not the same as those in the original text. Tokenizers'
constructors and reset() methods accept a CharFilter. CharFilters may
be chained to perform multiple pre-tokenization modifications.
Tokenizer
– A Tokenizer is a
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. However,
to modify text prior to tokenization, use a CharStream subclass (see
above).
TokenFilter
– A TokenFilter is
also a 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.
The synergy between Analyzer
and
Tokenizer
is sometimes confusing. To ease
this confusion, some clarifications:
Analyzer
is responsible for the entire task of
creating tokens out of the input text, while the Tokenizer
is only responsible for breaking the input text into tokens. Very likely, tokens created
by the Tokenizer
would be modified or even omitted
by the Analyzer
(via one or more
TokenFilter
s) before being returned.
Tokenizer
is a TokenStream
,
but Analyzer
is not.
Analyzer
is "field aware", but
Tokenizer
is not.
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:
PerFieldAnalyzerWrapper
– Most Analyzers perform the same operation on all
Field
s. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different
Field
s.
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 for testing out the speed of the analysis process.
Applications usually do not invoke analysis – Lucene does it for them:
addDocument(doc)
,
the Analyzer in effect for indexing is invoked for each indexed field of the added document.
However an application might invoke Analysis of any text for testing or for any other purpose, something like:
Version matchVersion = Version.LUCENE_XY; // Substitute desired Lucene version for XY
Analyzer analyzer = new StandardAnalyzer(matchVersion); // or any other analyzer
TokenStream ts = analyzer.tokenStream("myfield", new StringReader("some text goes here"));
OffsetAttribute offsetAtt = addAttribute(OffsetAttribute.class);
try {
ts.reset(); // Resets this stream to the beginning. (Required)
while (ts.incrementToken()) {
// Use AttributeSource.reflectAsString(boolean)
// for token stream debugging.
System.out.println("token: " + ts.reflectAsString(true));
System.out.println("token start offset: " + offsetAtt.startOffset());
System.out.println(" token end offset: " + offsetAtt.endOffset());
}
ts.end(); // Perform end-of-stream operations, e.g. set the final offset.
} finally {
ts.close(); // Release resources associated with this stream.
}
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. 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.
When 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
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
Analyzer.getPositionIncrementGap(fieldName)
:
Version matchVersion = Version.LUCENE_XY; // Substitute desired Lucene version for XY Analyzer myAnalyzer = new StandardAnalyzer(matchVersion) { public int getPositionIncrementGap(String fieldName) { return 10; } };
By default, all tokens created by Analyzers and Tokenizers have a
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
PositionIncrementAttribute.setPositionIncrement(int)
.
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 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.
A few more use cases for modifying position increments are:
"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 Attribute
and
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,
CharTermAttribute
contains the term text of a token, and
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.
Lucene provides seven Attributes out of the box:
CharTermAttribute |
The term text of a token. Implements CharSequence
(providing methods length() and charAt(), and allowing e.g. for direct
use with regular expression Matcher s) and
Appendable (allowing the term text to be appended to.)
|
OffsetAttribute |
The start and end offset of a token in characters. |
PositionIncrementAttribute |
See above for detailed information about position increment. |
PayloadAttribute |
The payload that a Token can optionally have. |
TypeAttribute |
The type of the token. Default is 'word'. |
FlagsAttribute |
Optional flags a token can have. |
KeywordAttribute |
Keyword-aware TokenStreams/-Filters skip modification of tokens that return true from this attribute's isKeyword() method. |
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.
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 ReusableAnalyzerBase { private Version matchVersion; public MyAnalyzer(Version matchVersion) { this.matchVersion = matchVersion; } @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 TokenStream API"; 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 CharTermAttribute termAtt = stream.addAttribute(CharTermAttribute.class); try { stream.reset(); // print all tokens until stream is exhausted while (stream.incrementToken()) { System.out.println(termAtt.toString()); } stream.end() } finally { 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 CharTermAttribute that the WhitespaceTokenizer provides. Here is the output:
This is a demo of the new TokenStream API
createComponents()
method in our analyzer needs to be changed:
@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:
This demo the new TokenStream APINow let's take a look how the LengthFilter is implemented (it is part of Lucene's core):
public final class LengthFilter extends FilteringTokenFilter { private final int min; private final int max; 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(boolean enablePositionIncrements, TokenStream in, int min, int max) { super(enablePositionIncrements, in); this.min = min; this.max = max; } /** * Build a filter that removes words that are too long or too * short from the text. * @deprecated Use {@link #LengthFilter(boolean, TokenStream, int, int)} instead. */ @Deprecated public LengthFilter(TokenStream in, int min, int max) { this(false, in, min, max); } @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 {@link #incrementToken}. */ protected abstract boolean accept() throws IOException; @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; } /** * @see #setEnablePositionIncrements(boolean) */ public boolean getEnablePositionIncrements() { return enablePositionIncrements; } /** * Iftrue
, 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 {@link QueryParser#setEnablePositionIncrements} if * you use QueryParser to create queries. */ public void setEnablePositionIncrements(boolean enable) { this.enablePositionIncrements = enable; } }
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 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:
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
AttributeImpl
:
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; } @Override public void clear() { pos = PartOfSpeech.Unknown; } @Override public void copyTo(AttributeImpl target) { ((PartOfSpeechAttribute) target).setPartOfSpeech(pos); } }
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 = addAttribute(PartOfSpeechAttribute.class); CharTermAttribute termAtt = addAttribute(CharTermAttribute.class); protected PartOfSpeechTaggingFilter(TokenStream input) { super(input); } public boolean incrementToken() throws IOException { if (!input.incrementToken()) {return false;} posAtt.setPartOfSpeech(determinePOS(termAtt.buffer(), 0, termAtt.length())); 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 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:
@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:
This demo the new TokenStream APIApparently 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 TokenStream API"; MyAnalyzer analyzer = new MyAnalyzer(); TokenStream stream = analyzer.tokenStream("field", new StringReader(text)); // get the CharTermAttribute from the TokenStream CharTermAttribute termAtt = stream.addAttribute(CharTermAttribute.class); // get the PartOfSpeechAttribute from the TokenStream PartOfSpeechAttribute posAtt = stream.addAttribute(PartOfSpeechAttribute.class); try { stream.reset(); // print all tokens until stream is exhausted while (stream.incrementToken()) { System.out.println(termAtt.toString() + ": " + posAtt.getPartOfSpeech()); } stream.end(); } finally { 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: NounEach 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 AttributeImpl implements FirstTokenOfSentenceAttribute { private boolean firstToken; public void setFirstToken(boolean firstToken) { this.firstToken = firstToken; } public boolean getFirstToken() { return firstToken; } @Override public void clear() { firstToken = false; } ...