## Package org.apache.lucene.analysis

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

See:
Description

Interface Summary
BaseTokenStreamTestCase.CheckClearAttributesAttribute: Attribute that records if it was cleared or not.

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`.
BaseTokenStreamTestCase: Base class for all Lucene unit tests that use TokenStreams.
BaseTokenStreamTestCase.CheckClearAttributesAttributeImpl: Attribute that records if it was cleared or not.
CachingTokenFilter: This class can be used if the token attributes of a TokenStream are intended to be consumed more than once.
CannedTokenStream: TokenStream from a canned list of Tokens.
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.
CollationTestBase: Base test class for testing Unicode collation.
EmptyTokenizer: Emits no tokens
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.
LookaheadTokenFilter<T extends LookaheadTokenFilter.Position>: An abstract TokenFilter to make it easier to build graph token filters requiring some lookahead.
LookaheadTokenFilter.Position: Holds all state for a single position; subclass this to record other state at each position.
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.
MockAnalyzer: Analyzer for testing
MockCharFilter: the purpose of this charfilter is to send offsets out of bounds if the analyzer doesn't use correctOffset or does incorrect offset math.
MockFixedLengthPayloadFilter: TokenFilter that adds random fixed-length payloads.
MockGraphTokenFilter: Randomly inserts overlapped (posInc=0) tokens with posLength sometimes > 1.
MockHoleInjectingTokenFilter
MockRandomLookaheadTokenFilter: Uses `LookaheadTokenFilter` to randomly peek at future tokens.
MockReaderWrapper: Wraps a Reader, and can throw random or fixed exceptions, and spoon feed read chars.
MockTokenizer: Tokenizer for testing.
MockVariableLengthPayloadFilter: TokenFilter that adds random variable-length payloads.
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`
StopAnalyzer: Filters `LetterTokenizer` with `LowerCaseFilter` and `StopFilter`.
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: A `TokenStream` enumerates the sequence of tokens, either from `Field`s of a `Document` or from query text.
TokenStreamToDot: Consumes a TokenStream and outputs the dot (graphviz) string (graph).
TypeTokenFilter: Removes tokens whose types appear in a set of blocked types from a token stream.
ValidatingTokenFilter: A TokenFilter that checks consistency of the tokens (eg offsets are consistent with one another).
VocabularyAssert: Utility class for doing vocabulary-based stemming tests
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, an 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. 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):

- Stemming – Replacing words with their stems. For instance with English stemming "bikes" is replaced with "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 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.

## Hints, Tips and Traps

The synergy between `Analyzer` and `Tokenizer` is sometimes confusing. To ease 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 `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:

1. `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.
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. 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, a QueryParser may invoke the Analyzer during parsing. 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:

        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.
        }

## 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. 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.

### 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)`:

      Version matchVersion = Version.LUCENE_XY; // Substitute desired Lucene version for XY
      Analyzer myAnalyzer = new StandardAnalyzer(matchVersion) {
        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 `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:

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.

## TokenStream API

"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.

### Attribute and AttributeSource

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.

### Using the TokenStream API

There are a few important things to know in order to use the new API efficiently which are summarized here. You may want to walk through the example below first and come back to this section afterwards.

1. Please keep in mind that an AttributeSource can only have one instance of a particular Attribute. Furthermore, if a chain of a TokenStream and multiple TokenFilters is used, then all TokenFilters in that chain share the Attributes with the TokenStream.
2. Attribute instances are reused for all tokens of a document. Thus, a TokenStream/-Filter needs to update the appropriate Attribute(s) in incrementToken(). The consumer, commonly the Lucene indexer, consumes the data in the Attributes and then calls incrementToken() again until it returns false, which indicates that the end of the stream was reached. This means that in each call of incrementToken() a TokenStream/-Filter can safely overwrite the data in the Attribute instances.
3. For performance reasons a TokenStream/-Filter should add/get Attributes during instantiation; i.e., create an attribute in the constructor and store references to it in an instance variable. Using an instance variable instead of calling addAttribute()/getAttribute() in incrementToken() will avoid attribute lookups for every token in the document.
4. All methods in AttributeSource are idempotent, which means calling them multiple times always yields the same result. This is especially important to know for addAttribute(). The method takes the type (`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.

### Example

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.

#### Whitespace tokenization

    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

#### Adding a LengthFilter

We want to suppress all tokens that have 2 or less characters. We can do that easily by adding a LengthFilter to the chain. Only the `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
    API

Now 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;
      }

      /**
       * 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 {@link QueryParser#setEnablePositionIncrements} if
       * you use QueryParser to create queries.
       */
      public void setEnablePositionIncrements(boolean enable) {
        this.enablePositionIncrements = enable;
      }
    }

#### Adding a custom Attribute

Now we're going to implement our own custom Attribute for part-of-speech tagging and call it consequently `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
    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 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: 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 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;
        }

      ...