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See:
Description
| Class Summary | |
|---|---|
| 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. |
| 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. |
| ISOLatin1AccentFilter | Deprecated. in favor of ASCIIFoldingFilter which covers a superset
of Latin 1. |
| KeywordAnalyzer | "Tokenizes" the entire stream as a single token. |
| 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. |
| 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. |
| SimpleAnalyzer | An Analyzer that filters LetterTokenizer
with LowerCaseFilter |
| SinkTokenizer | Deprecated. Use TeeSinkTokenFilter instead |
| StopAnalyzer | Filters LetterTokenizer with LowerCaseFilter and StopFilter. |
| StopFilter | Removes stop words from a token stream. |
| 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 | |
| TeeTokenFilter | Deprecated. Use TeeSinkTokenFilter instead |
| Token | A Token is an occurrence of a term from the text of a field. |
| TokenFilter | A TokenFilter is a TokenStream whose input is another TokenStream. |
| Tokenizer | A Tokenizer is a TokenStream whose input is a Reader. |
| TokenStream | A TokenStream enumerates the sequence of tokens, either from
Fields of a Document or from query text. |
| 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, 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:
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.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.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 on 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
TokenFilters) 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
Fields. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different
Fields.
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.
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.
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 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):
Analyzer myAnalyzer = new StandardAnalyzer() {
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:
public TokenStream tokenStream(final String fieldName, Reader reader) {
final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
TokenStream res = new TokenStream() {
TermAttribute termAtt = (TermAttribute) addAttribute(TermAttribute.class);
PositionIncrementAttribute posIncrAtt = (PositionIncrementAttribute) 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.
Attribute and
AttributeSource. An Attribute serves as a
particular piece of information about a text token. For example, TermAttribute
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 now provides six Attributes out of the box, which replace the variables the Token class has:
TermAttributeThe term text of a token.
OffsetAttributeThe start and end offset of token in characters.
PositionIncrementAttributeSee above for detailed information about position increment.
PayloadAttributeThe payload that a Token can optionally have.
TypeAttributeThe type of the token. Default is 'word'.
FlagsAttributeOptional 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 = (TermAttribute) 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 APINow 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 = (TermAttribute) 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 or downcasting
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. 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. 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;
}
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 = (PartOfSpeechAttribute) addAttribute(PartOfSpeechAttribute.class);
termAtt = (TermAttribute) 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 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 new TokenStream API";
MyAnalyzer analyzer = new MyAnalyzer();
TokenStream stream = analyzer.tokenStream("field", new StringReader(text));
// get the TermAttribute from the TokenStream
TermAttribute termAtt = (TermAttribute) stream.addAttribute(TermAttribute.class);
// get the PartOfSpeechAttribute from the TokenStream
PartOfSpeechAttribute posAtt = (PartOfSpeechAttribute) 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: 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 Attribute
implements FirstTokenOfSentenceAttribute {
private boolean firstToken;
public void setFirstToken(boolean firstToken) {
this.firstToken = firstToken;
}
public boolean getFirstToken() {
return firstToken;
}
public void clear() {
firstToken = false;
}
...
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