Package org.apache.lucene.analysis

API and code to convert text into indexable/searchable tokens.

See:
Description

Class Summary

Analyzer	An Analyzer builds TokenStreams, which analyze text.
CachingTokenFilter	This class can be used if the Tokens 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.
CharTokenizer	An abstract base class for simple, character-oriented tokenizers.
ISOLatin1AccentFilter	A filter that replaces accented characters in the ISO Latin 1 character set (ISO-8859-1) by their unaccented equivalent.
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 and 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.
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	A SinkTokenizer can be used to cache Tokens for use in an Analyzer
StopAnalyzer	Filters LetterTokenizer with LowerCaseFilter and StopFilter.
StopFilter	Removes stop words from a token stream.
TeeTokenFilter	Works in conjunction with the SinkTokenizer to provide the ability to set aside tokens that have already been analyzed.
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 token stream.
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.

Package org.apache.lucene.analysis Description

API and code to convert text into indexable/searchable tokens. Covers Analyzer and related classes.

Parsing? Tokenization? Analysis!

Lucene, indexing and search library, accepts only plain text input.

Parsing

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.

Tokenization

Plain text passed to Lucene for indexing goes through a process generally called tokenization – namely breaking of the input text into small indexing elements – Tokens. The way input text is broken into tokens very much dictates further capabilities of search upon 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 is needed, providing for several functions, including (but not limited to):

• Stemming – Replacing of words by their stems. For instance with English stemming "bikes" is replaced by "bike"; now query "bike" can find both documents containing "bike" and those containing "bikes".
• Stop Words Filtering – Common words like "the", "and" and "a" rarely add any value to a search. Removing them shrinks the index size and increases performance. It may also reduce some "noise" and actually improve search quality.
• Text Normalization – Stripping accents and other character markings can make for better searching.
• Synonym Expansion – Adding in synonyms at the same token position as the current word can mean better matching when users search with words in the synonym set.

Core Analysis

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

Hints, Tips and Traps

The synergy between Analyzer and Tokenizer is sometimes confusing. To ease on this confusion, some clarifications:

• The 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:

1. PerFieldAnalyzerWrapper – Most Analyzers perform the same operation on all Fields. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different Fields.
2. The contrib/analyzers library located at the root of the Lucene distribution has a number of different Analyzer implementations to solve a variety of different problems related to searching. Many of the Analyzers are designed to analyze non-English languages.
3. The contrib/snowball library located at the root of the Lucene distribution has Analyzer and TokenFilter implementations for a variety of Snowball stemmers. See http://snowball.tartarus.org for more information on Snowball stemmers.
4. There are a variety of Tokenizer and TokenFilter implementations in this package. Take a look around, chances are someone has implemented what you need.

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.

Invoking the Analyzer

Applications usually do not invoke analysis – Lucene does it for them:

• At indexing, as a consequence of addDocument(doc), the Analyzer in effect for indexing is invoked for each indexed field of the added document.
• At search, as a consequence of 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.

However an application might invoke Analysis of any text for testing or for any other purpose, something like:

      Analyzer analyzer = new StandardAnalyzer(); // or any other analyzer
      TokenStream ts = analyzer.tokenStream("myfield",new StringReader("some text goes here"));
      Token t = ts.next();
      while (t!=null) {
        System.out.println("token: "+t));
        t = ts.next();
      }
  

Indexing Analysis vs. Search Analysis

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:

1. Test test test... (did we say test?)
2. Beware of over analysis – might hurt indexing performance.
3. Start with same analyzer for indexing and search, otherwise searches would not find what they are supposed to...
4. In some cases a different analyzer is required for indexing and search, for instance:
   • Certain searches require more stop words to be filtered. (I.e. more than those that were filtered at indexing.)
   • Query expansion by synonyms, acronyms, auto spell correction, etc.
   This might sometimes require a modified analyzer – see the next section on how to do that.

Implementing your own Analyzer

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.

Field Section Boundaries

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;
         }
      };
  

Token Position Increments

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 Token.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() {
          public Token next() throws IOException {
            int extraIncrement = 0;
            while (true) {
              Token t = ts.next();
              if (t!=null) {
                if (stopWords.contains(t.termText())) {
                  extraIncrement++; // filter this word
                  continue;
                } 
                if (extraIncrement>0) {
                  t.setPositionIncrement(t.getPositionIncrement()+extraIncrement);
                }
              }
              return t;
            }
          }
        };
        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:

1. Inhibiting phrase and proximity matches in sentence boundaries – for this, a tokenizer that identifies a new sentence can add 1 to the position increment of the first token of the new sentence.
2. Injecting synonyms – here, synonyms of a token should be added after that token, and their position increment should be set to 0. As result, all synonyms of a token would be considered to appear in exactly the same position as that token, and so would they be seen by phrase and proximity searches.