org.apache.lucene/lucene-core/8.7.0 : org/apache/lucene/analysis/package-info.java

org.apache.lucene.analysis
https://lucene.apache.org/lucene-parent/lucene-core: Apache Lucene Java Core (The Apache Software Foundation)
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Text analysis. 
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 supplying a TokenStream which can be consumed by the indexing and searching processes. See below for more information on implementing your own Analyzer. Most of the time, you can use an anonymous subclass of Analyzer. 
   CharFilter – CharFilter extends Reader to transform the text before it is tokenized, 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. Tokenizer.setReader(Reader) accept CharFilters.  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 many cases, an Analyzer will use a Tokenizer as the first step in the analysis process. However, to modify text prior to tokenization, use a CharFilter subclass (see above). 
   TokenFilter – A TokenFilter is 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 case folding.  Not all Analyzers require TokenFilters.
  

Hints, Tips and Traps
 The relationship between Analyzer and CharFilters, Tokenizers, and TokenFilters is sometimes confusing. To ease this confusion, here is some clarifications: 

   The Analyzer is a factory for analysis chains. Analyzers don't
    process text, Analyzers construct CharFilters, Tokenizers, and/or
    TokenFilters that process text. An Analyzer has two tasks: to produce TokenStreams that accept a reader and produces tokens, and to wrap or otherwise pre-process Reader objects. 
   The CharFilter is a subclass of Reader that supports offset tracking. 
  TheTokenizer is only responsible for breaking the input text into tokens.
  
  TheTokenFilter modifies a stream of tokens and their contents. 
   Tokenizer is a TokenStream, but Analyzer is not. 
   Analyzer is "field aware", but Tokenizer is not. Analyzers may take a field name into account when constructing the TokenStream. 


  If you want to use a particular combination of CharFilters, a
  Tokenizer, and some TokenFilters, the simplest thing is often an create an anonymous subclass of Analyzer, provide Analyzer.createComponents(String) and perhaps also Analyzer.initReader(String, Reader). However, if you need the same set of components over and over in many places, you can make a subclass of Analyzer. In fact, Apache Lucene supplies a large family of Analyzer classes that deliver useful
  analysis chains. The most common of these is the StandardAnalyzer.
  Many applications will have a long and industrious life with nothing more
  than the StandardAnalyzer. The analyzers-common
  library provides many pre-existing analyzers for various languages.
  The analysis-common library also allows to configure a custom Analyzer without subclassing using the
  CustomAnalyzer
  class.


  Aside from the StandardAnalyzer,
  Lucene includes several components containing analysis components,
  all under the 'analysis' directory of the distribution. Some of
  these support particular languages, others integrate external
  components. The 'common' subdirectory has some noteworthy
 general-purpose analyzers, including the PerFieldAnalyzerWrapper. Most Analyzers perform the same operation on all Fields. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different Fields. There is a great deal of functionality in the analysis area, you should study it carefully to find the pieces you need. 

  Analysis is one of the main causes of slow 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 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. Applications construct Analyzers and pass then into Lucene,
 as follows:


   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")); // The Analyzer class will construct the Tokenizer, TokenFilter(s), and CharFilter(s), // and pass the resulting Reader to the Tokenizer. OffsetAttribute offsetAtt = ts.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 for your application will depend on what your input text
  looks like and what problem you are trying to solve.
  Lucene java's wiki page 
  AnalysisParalysis 
  provides some data on "analyzing your analyzer".
  Here are some rules of thumb:
  

    Test test test... (did we say test?)
    Beware of too much analysis – it might hurt indexing performance.
    Start with the same analyzer for indexing and search, otherwise searches would not find what they are supposed to...
    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 and Analysis Components
 Creating your own Analyzer is straightforward. Your Analyzer should subclass Analyzer. It can use 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
  analyzers-common 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;
    }
  };

End of Input Cleanup
 At the ends of each field, Lucene will call the TokenStream.end(). The components of the token stream (the tokenizer and the token filters) must
   put accurate values into the token attributes to reflect the situation at the end of the field.
   The Offset attribute must contain the final offset (the total number of characters processed)
   in both start and end. Attributes like PositionLength must be correct. 

 The base methodTokenStream.end() sets PositionIncrement to 0, which is required. Other components must override this method to fix up the other attributes. 
Token Position Increments
 By default, TokenStream arranges for the position increment of all tokens to be 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") = 3 + 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 the phrase query "blue sky" would not find that document because the
   position increment between "blue" and "sky" is only 1.

   
   If this behavior does not fit the application needs, the query parser needs to be
   configured to not take position increments into account when generating phrase queries.


  Note that a filter that filters out tokens must increment the position increment in order not to generate corrupt
  tokenstream graphs. Here is the logic used by StopFilter to increment positions when filtering out tokens:

  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 += posIncrAtt.getPositionIncrement(); // filter this word
              continue;
            } 
            if (extraIncrement > 0) {
              posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
            }
          }
          return hasNext;
        }
      }
    };
    return res;
  }


   A few more use cases for modifying position increments are:


  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.
  Injecting synonyms – synonyms of a token should be created at the same position as the original token, and the output order of the original token and the injected synonym is undefined as long as they both leave from the same position. 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. For multi-token synonyms to work correctly, you should use SynoymGraphFilter at search time only.

Token Position Length
 By default, all tokens created by Analyzers and Tokenizers have a position length of one. This means that the token occupies a single position. This attribute is not indexed and thus not taken into account for positional queries, but is used by eg. suggesters. 

   The main use case for positions lengths is multi-word synonyms. With single-word
   synonyms, setting the position increment to 0 is enough to denote the fact that two
   words are synonyms, for example:


Term red magenta
Position increment 1 0


   Given that position(magenta) = 0 + position(red), they are at the same position, so anything
   working with analyzers will return the exact same result if you replace "magenta" with "red"
   in the input. However, multi-word synonyms are more tricky. Let's say that you want to build
   a TokenStream where "IBM" is a synonym of "Internal Business Machines". Position increments
   are not enough anymore:


Term IBM International Business Machines
Position increment 1 0 1 1


   The problem with this token stream is that "IBM" is at the same position as "International"
   although it is a synonym with "International Business Machines" as a whole. Setting
   the position increment of "Business" and "Machines" to 0 wouldn't help as it would mean
   than "International" is a synonym of "Business". The only way to solve this issue is to
   make "IBM" span across 3 positions, this is where position lengths come to rescue.


Term IBM International Business Machines
Position increment 1 0 1 1
Position length 3 1 1 1


   This new attribute makes clear that "IBM" and "International Business Machines" start and end
   at the same positions.


How to not write corrupt token streams

   There are a few rules to observe when writing custom Tokenizers and TokenFilters:


  The first position increment must be > 0.
  Positions must not go backward.
  Tokens that have the same start position must have the same start offset.
  Tokens that have the same end position (taking into account the
  position length) must have the same end offset.
  Tokenizers must call AttributeSource.clearAttributes() in incrementToken().
  Tokenizers must override TokenStream.end(), and pass the final offset (the total number of input characters processed) to both parameters of OffsetAttribute.setOffset(int, int).


   Although these rules might seem easy to follow, problems can quickly happen when chaining
   badly implemented filters that play with positions and offsets, such as synonym or n-grams
   filters. Here are good practices for writing correct filters:


  Token filters should not modify offsets. If you feel that your filter would need to modify offsets, then it should probably be implemented as a tokenizer.
  Token filters should not insert positions. If a filter needs to add tokens, then they should all have a position increment of 0.
  When they add tokens, token filters should call AttributeSource.clearAttributes() first.
  When they remove tokens, token filters should increment the position increment of the following token.
  Token filters should preserve position lengths.

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 the analysis API must transport custom types of
  data from the documents to the indexer. (It also supports communications
  amongst the analysis components.)

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 Matchers) 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.
  
  
    PositionLengthAttribute
    The number of positions occupied by a token.
  
  
    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. 
    
  

More Requirements for Analysis Component Classes
Due to the historical development of the API, there are some perhaps
less than obvious requirements to implement analysis components
classes.
Token Stream Lifetime
The code fragment of the analysis workflow
protocol above shows a token stream being obtained, used, and then left for garbage. However, that does not mean that the components of that token stream will, in fact, be discarded. The default is just the opposite. Analyzer applies a reuse strategy to the tokenizer and the token filters. It will reuse them. For each new input, it calls Tokenizer.setReader(Reader) to set the input. Your components must be prepared for this scenario, as described below. Tokenizer

   You should create your tokenizer class by extending Tokenizer. 
  
  Your tokenizer must override TokenStream.end(). Your implementation must call
  super.end(). It must set a correct final offset into
  the offset attribute, and finish up and other attributes to reflect
  the end of the stream.
  
   If your tokenizer overrides TokenStream.reset() or TokenStream.close(), it must call the corresponding superclass method.
  

Token Filter
 You should create your token filter class by extending TokenFilter. If your token filter overrides TokenStream.reset(), TokenStream.end() or TokenStream.close(), it must call the corresponding superclass method.
Creating delegates
 Forwarding classes (those which extend Tokenizer but delegate selected logic to another tokenizer) must also set the reader to the delegate in the overridden Tokenizer.reset() method, e.g.:  public class ForwardingTokenizer extends Tokenizer { private Tokenizer delegate; ... @Override public void reset() { super.reset(); delegate.setReader(this.input); delegate.reset(); } } 
Testing Your Analysis Component

    The lucene-test-framework component defines
    BaseTokenStreamTestCase. By extending
    this class, you can create JUnit tests that validate that your
    Analyzer and/or analysis components correctly implement the
    protocol. The checkRandomData methods of that class are particularly effective in flushing out errors.

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.

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.


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.


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.


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 Analyzer { private Version matchVersion; public MyAnalyzer(Version matchVersion) { this.matchVersion = matchVersion; } @Override protected TokenStreamComponents createComponents(String fieldName) { return new TokenStreamComponents(new WhitespaceTokenizer(matchVersion)); } 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) { final Tokenizer source = new WhitespaceTokenizer(matchVersion); TokenStream result = new LengthFilter(true, 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:
public final class LengthFilter extends FilteringTokenFilter { private final int min; private final int max; private final CharTermAttribute termAtt = addAttribute(CharTermAttribute.class); /** * Create a new LengthFilter. This will filter out tokens whose * CharTermAttribute is either too short * (< min) or too long (> max). * @param version the Lucene match version * @param in the TokenStream to consume * @param min the minimum length * @param max the maximum length */ public LengthFilter(Version version, TokenStream in, int min, int max) { super(version, in); this.min = min; this.max = max; } @Override public boolean accept() { 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 necessary. 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); /** * Create a new FilteringTokenFilter. * @param in the TokenStream to consume */ public FilteringTokenFilter(Version version, TokenStream in) { super(in); } /** Override this method and return if the current input token should be returned by incrementToken. */ protected abstract boolean accept() throws IOException; @Override public final boolean incrementToken() throws IOException { int skippedPositions = 0; while (input.incrementToken()) { if (accept()) { if (skippedPositions != 0) { posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement() + skippedPositions); } return true; } skippedPositions += posIncrAtt.getPositionIncrement(); } // reached EOS -- return false return false; } @Override public void reset() throws IOException { super.reset(); } } 
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: 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) { final Tokenizer source = new WhitespaceTokenizer(matchVersion); TokenStream result = new LengthFilter(true, 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 exercise. 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; } ... 
Adding a CharFilter chain
 Analyzers take Java Readers as input. Of course you can wrap your Readers with FilterReaders to manipulate content, but this would have the big disadvantage that character offsets might be inconsistent with your original text.  CharFilter is designed to allow you to pre-process input like a FilterReader would, but also preserve the original offsets associated with those characters. This way mechanisms like highlighting still work correctly. CharFilters can be chained. 

Example:
public class MyAnalyzer extends Analyzer { @Override protected TokenStreamComponents createComponents(String fieldName) { return new TokenStreamComponents(new MyTokenizer()); } @Override protected Reader initReader(String fieldName, Reader reader) { // wrap the Reader in a CharFilter chain. return new SecondCharFilter(new FirstCharFilter(reader)); } } 
/**
 * Text analysis. 
 * <p>API and code to convert text into indexable/searchable tokens.  Covers {@link org.apache.lucene.analysis.Analyzer} and related classes.</p>
 * <h2>Parsing? Tokenization? Analysis!</h2>
 * <p>
 * Lucene, an indexing and search library, accepts only plain text input.
 * <h2>Parsing</h2>
 * <p>
 * Applications that build their search capabilities upon Lucene may support documents in various formats &ndash; HTML, XML, PDF, Word &ndash; just to name a few.
 * Lucene does not care about the <i>Parsing</i> of these and other document formats, and it is the responsibility of the 
 * application using Lucene to use an appropriate <i>Parser</i> to convert the original format into plain text before passing that plain text to Lucene.
 * <h2>Tokenization</h2>
 * <p>
 * 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 &ndash; 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).
 * <p>
 *   In some cases simply breaking the input text into tokens is not enough
 *   &ndash; a deeper <i>Analysis</i> may be needed. Lucene includes both
 *   pre- and post-tokenization analysis facilities.
 * </p>
 * <p>
 *   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.
 * </p>
 * <p>
 *   There are many post-tokenization steps that can be done, including 
 *   (but not limited to):
 * </p>
 * <ul>
 *   <li><a href="http://en.wikipedia.org/wiki/Stemming">Stemming</a> &ndash; 
 *       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".
 *   </li>
 *   <li><a href="http://en.wikipedia.org/wiki/Stop_words">Stop Words Filtering</a> &ndash; 
 *       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.
 *   </li>
 *   <li><a href="http://en.wikipedia.org/wiki/Text_normalization">Text Normalization</a> &ndash; 
 *       Stripping accents and other character markings can make for better searching.
 *   </li>
 *   <li><a href="http://en.wikipedia.org/wiki/Synonym">Synonym Expansion</a> &ndash; 
 *       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.
 *   </li>
 * </ul>
 * <h2>Core Analysis</h2>
 * <p>
 *   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:
 * </p>
 * <ul>
 *   <li>
 *     {@link org.apache.lucene.analysis.Analyzer} &ndash; An <code>Analyzer</code> is 
 *     responsible for supplying a
 *     {@link org.apache.lucene.analysis.TokenStream} which can be consumed
 *     by the indexing and searching processes.  See below for more information
 *     on implementing your own {@link org.apache.lucene.analysis.Analyzer}. Most of the time, you can use
 *     an anonymous subclass of {@link org.apache.lucene.analysis.Analyzer}.
 *   </li>
 *   <li>
 *     {@link org.apache.lucene.analysis.CharFilter} &ndash; <code>CharFilter</code> extends
 *     {@link java.io.Reader} to transform the text before it is
 *     tokenized, 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 <code>CharFilter</code>-modified text with offsets
 *     that are not the same as those in the original text. {@link org.apache.lucene.analysis.Tokenizer#setReader(java.io.Reader)}
 *     accept <code>CharFilter</code>s.  <code>CharFilter</code>s may
 *     be chained to perform multiple pre-tokenization modifications.
 *   </li>
 *   <li>
 *     {@link org.apache.lucene.analysis.Tokenizer} &ndash; A <code>Tokenizer</code> is a 
 *     {@link org.apache.lucene.analysis.TokenStream} and is responsible for
 *     breaking up incoming text into tokens. In many cases, an {@link org.apache.lucene.analysis.Analyzer} will
 *     use a {@link org.apache.lucene.analysis.Tokenizer} as the first step in the analysis process.  However,
 *     to modify text prior to tokenization, use a {@link org.apache.lucene.analysis.CharFilter} subclass (see
 *     above).
 *   </li>
 *   <li>
 *     {@link org.apache.lucene.analysis.TokenFilter} &ndash; A <code>TokenFilter</code> is
 *     a {@link org.apache.lucene.analysis.TokenStream} and is responsible
 *     for modifying tokens that have been created by the <code>Tokenizer</code>. Common 
 *     modifications performed by a <code>TokenFilter</code> are: deletion, stemming, synonym 
 *     injection, and case folding.  Not all <code>Analyzer</code>s require <code>TokenFilter</code>s.
 *   </li>
 * </ul>
 * <h2>Hints, Tips and Traps</h2>
 * <p>
 *   The relationship between {@link org.apache.lucene.analysis.Analyzer} and 
 *   {@link org.apache.lucene.analysis.CharFilter}s,
 *   {@link org.apache.lucene.analysis.Tokenizer}s,
 *   and {@link org.apache.lucene.analysis.TokenFilter}s is sometimes confusing. To ease
 *   this confusion, here is some clarifications:
 * </p>
 * <ul>
 *   <li>
 *     The {@link org.apache.lucene.analysis.Analyzer} is a
 *     <strong>factory</strong> for analysis chains. <code>Analyzer</code>s don't
 *     process text, <code>Analyzer</code>s construct <code>CharFilter</code>s, <code>Tokenizer</code>s, and/or
 *     <code>TokenFilter</code>s that process text. An <code>Analyzer</code> has two tasks: 
 *     to produce {@link org.apache.lucene.analysis.TokenStream}s that accept a
 *     reader and produces tokens, and to wrap or otherwise
 *     pre-process {@link java.io.Reader} objects.
 *   </li>
 *   <li>
 *   The {@link org.apache.lucene.analysis.CharFilter} is a subclass of
 *  {@link java.io.Reader} that supports offset tracking.
 *   </li>
 *   <li>The{@link org.apache.lucene.analysis.Tokenizer}
 *     is only responsible for <u>breaking</u> the input text into tokens.
 *   </li>
 *   <li>The{@link org.apache.lucene.analysis.TokenFilter} modifies a
 *   stream of tokens and their contents.
 *   </li>
 *   <li>
 *     {@link org.apache.lucene.analysis.Tokenizer} is a {@link org.apache.lucene.analysis.TokenStream}, 
 *     but {@link org.apache.lucene.analysis.Analyzer} is not.
 *   </li>
 *   <li>
 *     {@link org.apache.lucene.analysis.Analyzer} is "field aware", but 
 *     {@link org.apache.lucene.analysis.Tokenizer} is not. {@link org.apache.lucene.analysis.Analyzer}s may
 *     take a field name into account when constructing the {@link org.apache.lucene.analysis.TokenStream}.
 *   </li>
 * </ul>
 * <p>
 *   If you want to use a particular combination of <code>CharFilter</code>s, a
 *   <code>Tokenizer</code>, and some <code>TokenFilter</code>s, the simplest thing is often an
 *   create an anonymous subclass of {@link org.apache.lucene.analysis.Analyzer}, provide {@link
 *   org.apache.lucene.analysis.Analyzer#createComponents(String)} and perhaps also
 *   {@link org.apache.lucene.analysis.Analyzer#initReader(String,
 *   java.io.Reader)}. However, if you need the same set of components
 *   over and over in many places, you can make a subclass of
 *   {@link org.apache.lucene.analysis.Analyzer}. In fact, Apache Lucene
 *   supplies a large family of <code>Analyzer</code> classes that deliver useful
 *   analysis chains. The most common of these is the <a href="{@docRoot}/org/apache/lucene/analysis/standard/StandardAnalyzer.html">StandardAnalyzer</a>.
 *   Many applications will have a long and industrious life with nothing more
 *   than the <code>StandardAnalyzer</code>. The <a href="{@docRoot}/../analyzers-common/overview-summary.html">analyzers-common</a>
 *   library provides many pre-existing analyzers for various languages.
 *   The analysis-common library also allows to configure a custom Analyzer without subclassing using the
 *   <a href="{@docRoot}/../analyzers-common/org/apache/lucene/analysis/custom/CustomAnalyzer.html">CustomAnalyzer</a>
 *   class.
 * </p>
 * <p>
 *   Aside from the <code>StandardAnalyzer</code>,
 *   Lucene includes several components containing analysis components,
 *   all under the 'analysis' directory of the distribution. Some of
 *   these support particular languages, others integrate external
 *   components. The 'common' subdirectory has some noteworthy
 *  general-purpose analyzers, including the <a href="{@docRoot}/../analyzers-common/org/apache/lucene/analysis/miscellaneous/PerFieldAnalyzerWrapper.html">PerFieldAnalyzerWrapper</a>. Most <code>Analyzer</code>s perform the same operation on all
 *  {@link org.apache.lucene.document.Field}s.  The PerFieldAnalyzerWrapper can be used to associate a different <code>Analyzer</code> with different
 *  {@link org.apache.lucene.document.Field}s. There is a great deal of
 *  functionality in the analysis area, you should study it carefully to
 *  find the pieces you need.
 * </p>
 * <p>
 *   Analysis is one of the main causes of slow 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 benchmark/ library can be useful 
 *   for testing out the speed of the analysis process.
 * </p>
 * <h2>Invoking the Analyzer</h2>
 * <p>
 *   Applications usually do not invoke analysis &ndash; Lucene does it
 *  for them. Applications construct <code>Analyzer</code>s and pass then into Lucene,
 *  as follows:
 * </p>
 * <ul>
 *   <li>
 *     At indexing, as a consequence of 
 *     {@link org.apache.lucene.index.IndexWriter#addDocument(Iterable) addDocument(doc)},
 *     the <code>Analyzer</code> in effect for indexing is invoked for each indexed field of the added document.
 *   </li>
 *   <li>
 *     At search, a <code>QueryParser</code> may invoke the Analyzer during parsing.  Note that for some queries, analysis does not
 *     take place, e.g. wildcard queries.
 *   </li>
 * </ul>
 * <p>
 *   However an application might invoke Analysis of any text for testing or for any other purpose, something like:
 * </p>
 * <PRE class="prettyprint" id="analysis-workflow">
 *     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"));
 *     // The Analyzer class will construct the Tokenizer, TokenFilter(s), and CharFilter(s),
 *     //   and pass the resulting Reader to the Tokenizer.
 *     OffsetAttribute offsetAtt = ts.addAttribute(OffsetAttribute.class);
 *     
 *     try {
 *       ts.reset(); // Resets this stream to the beginning. (Required)
 *       while (ts.incrementToken()) {
 *         // Use {@link org.apache.lucene.util.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.
 *     }
 * </PRE>
 * <h2>Indexing Analysis vs. Search Analysis</h2>
 * <p>
 *   Selecting the "correct" analyzer is crucial
 *   for search quality, and can also affect indexing and search performance.
 *   The "correct" analyzer for your application will depend on what your input text
 *   looks like and what problem you are trying to solve.
 *   Lucene java's wiki page 
 *   <a href="http://wiki.apache.org/lucene-java/AnalysisParalysis">AnalysisParalysis</a> 
 *   provides some data on "analyzing your analyzer".
 *   Here are some rules of thumb:
 *   <ol>
 *     <li>Test test test... (did we say test?)</li>
 *     <li>Beware of too much analysis &ndash; it might hurt indexing performance.</li>
 *     <li>Start with the same analyzer for indexing and search, otherwise searches would not find what they are supposed to...</li>
 *     <li>In some cases a different analyzer is required for indexing and search, for instance:
 *         <ul>
 *            <li>Certain searches require more stop words to be filtered. (i.e. more than those that were filtered at indexing.)</li>
 *            <li>Query expansion by synonyms, acronyms, auto spell correction, etc.</li>
 *         </ul>
 *         This might sometimes require a modified analyzer &ndash; see the next section on how to do that.
 *     </li>
 *   </ol>
 * <h2>Implementing your own Analyzer and Analysis Components</h2>
 * <p>
 *   Creating your own Analyzer is straightforward. Your Analyzer should subclass {@link org.apache.lucene.analysis.Analyzer}. It can use
 *   existing analysis components &mdash; CharFilter(s) <i>(optional)</i>, a
 *   Tokenizer, and TokenFilter(s) <i>(optional)</i> &mdash; 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
 *   <a href="{@docRoot}/../analyzers-common/overview-summary.html">analyzers-common</a> library and/or ask on the 
 *   <a href="http://lucene.apache.org/core/discussion.html">java-user@lucene.apache.org mailing list</a> 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.
 * </p>
 * <p>
 *   The following sections discuss some aspects of implementing your own analyzer.
 * </p>
 * <h3>Field Section Boundaries</h3>
 * <p>
 *   When {@link org.apache.lucene.document.Document#add(org.apache.lucene.index.IndexableField) document.add(field)}
 *   is called multiple times for the same field name, we could say that each such call creates a new 
 *   section for that field in that document. 
 *   In fact, a separate call to 
 *   {@link org.apache.lucene.analysis.Analyzer#tokenStream(java.lang.String, java.io.Reader) tokenStream(field,reader)}
 *   would take place for each of these so called "sections".
 *   However, the default Analyzer behavior is to treat all these sections as one large section. 
 *   This allows phrase search and proximity search to seamlessly cross 
 *   boundaries between these "sections".
 *   In other words, if a certain field "f" is added like this:
 * </p>
 * <PRE class="prettyprint">
 *     document.add(new Field("f","first ends",...);
 *     document.add(new Field("f","starts two",...);
 *     indexWriter.addDocument(document);
 * </PRE>
 * <p>
 *   Then, a phrase search for "ends starts" would find that document.
 *   Where desired, this behavior can be modified by introducing a "position gap" between consecutive field "sections", 
 *   simply by overriding 
 *   {@link org.apache.lucene.analysis.Analyzer#getPositionIncrementGap(java.lang.String) Analyzer.getPositionIncrementGap(fieldName)}:
 * </p>
 * <PRE class="prettyprint">
 *   Version matchVersion = Version.LUCENE_XY; // Substitute desired Lucene version for XY
 *   Analyzer myAnalyzer = new StandardAnalyzer(matchVersion) {
 *     public int getPositionIncrementGap(String fieldName) {
 *       return 10;
 *     }
 *   };
 * </PRE>
 * <h3>End of Input Cleanup</h3>
 * <p>
 *    At the ends of each field, Lucene will call the {@link org.apache.lucene.analysis.TokenStream#end()}.
 *    The components of the token stream (the tokenizer and the token filters) <strong>must</strong>
 *    put accurate values into the token attributes to reflect the situation at the end of the field.
 *    The Offset attribute must contain the final offset (the total number of characters processed)
 *    in both start and end. Attributes like PositionLength must be correct. 
 * </p>
 * <p>
 *    The base method{@link org.apache.lucene.analysis.TokenStream#end()} sets PositionIncrement to 0, which is required.
 *    Other components must override this method to fix up the other attributes.
 * </p>
 * <h3>Token Position Increments</h3>
 * <p>
 *    By default, TokenStream arranges for the 
 *    {@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute#getPositionIncrement() position increment} of all tokens to be 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.
 * </p>
 * <p>
 *    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") = 3 + 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 the phrase query "blue sky" would not find that document because the
 *    position increment between "blue" and "sky" is only 1.
 * </p>
 * <p>   
 *    If this behavior does not fit the application needs, the query parser needs to be
 *    configured to not take position increments into account when generating phrase queries.
 * </p>
 * <p>
 *   Note that a filter that filters <strong>out</strong> tokens <strong>must</strong> increment the position increment in order not to generate corrupt
 *   tokenstream graphs. Here is the logic used by StopFilter to increment positions when filtering out tokens:
 * </p>
 * <PRE class="prettyprint">
 *   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 += posIncrAtt.getPositionIncrement(); // filter this word
 *               continue;
 *             } 
 *             if (extraIncrement &gt; 0) {
 *               posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement()+extraIncrement);
 *             }
 *           }
 *           return hasNext;
 *         }
 *       }
 *     };
 *     return res;
 *   }
 * </PRE>
 * <p>
 *    A few more use cases for modifying position increments are:
 * </p>
 * <ol>
 *   <li>Inhibiting phrase and proximity matches in sentence boundaries &ndash; for this, a tokenizer that 
 *       identifies a new sentence can add 1 to the position increment of the first token of the new sentence.</li>
 *   <li>Injecting synonyms &ndash; synonyms of a token should be created at the same position as the
 *       original token, and the output order of the original token and the injected synonym is undefined
 *       as long as they both leave from the same position.  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.  For multi-token synonyms to work correctly, you should use
 *       {@code SynoymGraphFilter} at search time only.</li>
 * </ol>
 * 
 * <h3>Token Position Length</h3>
 * <p>
 *    By default, all tokens created by Analyzers and Tokenizers have a
 *    {@link org.apache.lucene.analysis.tokenattributes.PositionLengthAttribute#getPositionLength() position length} of one.
 *    This means that the token occupies a single position. This attribute is not indexed
 *    and thus not taken into account for positional queries, but is used by eg. suggesters.
 * </p>
 * <p>
 *    The main use case for positions lengths is multi-word synonyms. With single-word
 *    synonyms, setting the position increment to 0 is enough to denote the fact that two
 *    words are synonyms, for example:
 * </p>
 * <table summary="table showing position increments of 1 and 0 for red and magenta, respectively">
 * <tr><td>Term</td><td>red</td><td>magenta</td></tr>
 * <tr><td>Position increment</td><td>1</td><td>0</td></tr>
 * </table>
 * <p>
 *    Given that position(magenta) = 0 + position(red), they are at the same position, so anything
 *    working with analyzers will return the exact same result if you replace "magenta" with "red"
 *    in the input. However, multi-word synonyms are more tricky. Let's say that you want to build
 *    a TokenStream where "IBM" is a synonym of "Internal Business Machines". Position increments
 *    are not enough anymore:
 * </p>
 * <table summary="position increments where international is zero">
 * <tr><td>Term</td><td>IBM</td><td>International</td><td>Business</td><td>Machines</td></tr>
 * <tr><td>Position increment</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>
 * </table>
 * <p>
 *    The problem with this token stream is that "IBM" is at the same position as "International"
 *    although it is a synonym with "International Business Machines" as a whole. Setting
 *    the position increment of "Business" and "Machines" to 0 wouldn't help as it would mean
 *    than "International" is a synonym of "Business". The only way to solve this issue is to
 *    make "IBM" span across 3 positions, this is where position lengths come to rescue.
 * </p>
 * <table summary="position lengths where IBM is three">
 * <tr><td>Term</td><td>IBM</td><td>International</td><td>Business</td><td>Machines</td></tr>
 * <tr><td>Position increment</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>
 * <tr><td>Position length</td><td>3</td><td>1</td><td>1</td><td>1</td></tr>
 * </table>
 * <p>
 *    This new attribute makes clear that "IBM" and "International Business Machines" start and end
 *    at the same positions.
 * </p>
 * <a name="corrupt"></a>
 * <h3>How to not write corrupt token streams</h3>
 * <p>
 *    There are a few rules to observe when writing custom Tokenizers and TokenFilters:
 * </p>
 * <ul>
 *   <li>The first position increment must be &gt; 0.</li>
 *   <li>Positions must not go backward.</li>
 *   <li>Tokens that have the same start position must have the same start offset.</li>
 *   <li>Tokens that have the same end position (taking into account the
 *   position length) must have the same end offset.</li>
 *   <li>Tokenizers must call {@link
 *   org.apache.lucene.util.AttributeSource#clearAttributes()} in
 *   incrementToken().</li>
 *   <li>Tokenizers must override {@link
 *   org.apache.lucene.analysis.TokenStream#end()}, and pass the final
 *   offset (the total number of input characters processed) to both
 *   parameters of {@link org.apache.lucene.analysis.tokenattributes.OffsetAttribute#setOffset(int, int)}.</li>
 * </ul>
 * <p>
 *    Although these rules might seem easy to follow, problems can quickly happen when chaining
 *    badly implemented filters that play with positions and offsets, such as synonym or n-grams
 *    filters. Here are good practices for writing correct filters:
 * </p>
 * <ul>
 *   <li>Token filters should not modify offsets. If you feel that your filter would need to modify offsets, then it should probably be implemented as a tokenizer.</li>
 *   <li>Token filters should not insert positions. If a filter needs to add tokens, then they should all have a position increment of 0.</li>
 *   <li>When they add tokens, token filters should call {@link org.apache.lucene.util.AttributeSource#clearAttributes()} first.</li>
 *   <li>When they remove tokens, token filters should increment the position increment of the following token.</li>
 *   <li>Token filters should preserve position lengths.</li>
 * </ul>
 * <h2>TokenStream API</h2>
 * <p>
 *   "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 the analysis API must transport custom types of
 *   data from the documents to the indexer. (It also supports communications
 *   amongst the analysis components.)
 * </p>
 * <h3>Attribute and AttributeSource</h3>
 * <p>
 *   Classes {@link org.apache.lucene.util.Attribute} and 
 *   {@link org.apache.lucene.util.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, 
 *   {@link org.apache.lucene.analysis.tokenattributes.CharTermAttribute} 
 *   contains the term text of a token, and 
 *   {@link org.apache.lucene.analysis.tokenattributes.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.
 * </p>
 * <p>
 * Lucene provides seven Attributes out of the box:
 * </p>
 * <table rules="all" frame="box" cellpadding="3" summary="common bundled attributes">
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.CharTermAttribute}</td>
 *     <td>
 *       The term text of a token.  Implements {@link java.lang.CharSequence} 
 *       (providing methods length() and charAt(), and allowing e.g. for direct
 *       use with regular expression {@link java.util.regex.Matcher}s) and 
 *       {@link java.lang.Appendable} (allowing the term text to be appended to.)
 *     </td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.OffsetAttribute}</td>
 *     <td>The start and end offset of a token in characters.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute}</td>
 *     <td>See above for detailed information about position increment.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.PositionLengthAttribute}</td>
 *     <td>The number of positions occupied by a token.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.PayloadAttribute}</td>
 *     <td>The payload that a Token can optionally have.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.TypeAttribute}</td>
 *     <td>The type of the token. Default is 'word'.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.FlagsAttribute}</td>
 *     <td>Optional flags a token can have.</td>
 *   </tr>
 *   <tr>
 *     <td>{@link org.apache.lucene.analysis.tokenattributes.KeywordAttribute}</td>
 *     <td>
 *       Keyword-aware TokenStreams/-Filters skip modification of tokens that
 *       return true from this attribute's isKeyword() method. 
 *     </td>
 *   </tr>
 * </table>
 * <h3>More Requirements for Analysis Component Classes</h3>
 * Due to the historical development of the API, there are some perhaps
 * less than obvious requirements to implement analysis components
 * classes.
 * <h4 id="analysis-lifetime">Token Stream Lifetime</h4>
 * The code fragment of the <a href="#analysis-workflow">analysis workflow
 * protocol</a> above shows a token stream being obtained, used, and then
 * left for garbage. However, that does not mean that the components of
 * that token stream will, in fact, be discarded. The default is just the
 * opposite. {@link org.apache.lucene.analysis.Analyzer} applies a reuse
 * strategy to the tokenizer and the token filters. It will reuse
 * them. For each new input, it calls {@link org.apache.lucene.analysis.Tokenizer#setReader(java.io.Reader)} 
 * to set the input. Your components must be prepared for this scenario,
 * as described below.
 * <h4>Tokenizer</h4>
 * <ul>
 *   <li>
 *   You should create your tokenizer class by extending {@link org.apache.lucene.analysis.Tokenizer}.
 *   </li>
 *   <li>
 *   Your tokenizer <strong>must</strong> override {@link org.apache.lucene.analysis.TokenStream#end()}.
 *   Your implementation <strong>must</strong> call
 *   <code>super.end()</code>. It must set a correct final offset into
 *   the offset attribute, and finish up and other attributes to reflect
 *   the end of the stream.
 *   </li>
 *   <li>
 *   If your tokenizer overrides {@link org.apache.lucene.analysis.TokenStream#reset()}
 *   or {@link org.apache.lucene.analysis.TokenStream#close()}, it
 *   <strong>must</strong> call the corresponding superclass method.
 *   </li>
 * </ul>
 * <h4>Token Filter</h4>
 *   You should create your token filter class by extending {@link org.apache.lucene.analysis.TokenFilter}.
 *   If your token filter overrides {@link org.apache.lucene.analysis.TokenStream#reset()},
 *   {@link org.apache.lucene.analysis.TokenStream#end()}
 *   or {@link org.apache.lucene.analysis.TokenStream#close()}, it
 *   <strong>must</strong> call the corresponding superclass method.
 * <h4>Creating delegates</h4>
 *   Forwarding classes (those which extend {@link org.apache.lucene.analysis.Tokenizer} but delegate
 *   selected logic to another tokenizer) must also set the reader to the delegate in the overridden
 *   {@link org.apache.lucene.analysis.Tokenizer#reset()} method, e.g.:
 *   <pre class="prettyprint">
 *     public class ForwardingTokenizer extends Tokenizer {
 *        private Tokenizer delegate;
 *        ...
 *        {@literal @Override}
 *        public void reset() {
 *           super.reset();
 *           delegate.setReader(this.input);
 *           delegate.reset();
 *        }
 *     }
 *   </pre>
 * <h3>Testing Your Analysis Component</h3>
 * <p>
 *     The lucene-test-framework component defines
 *     <a href="{@docRoot}/../test-framework/org/apache/lucene/analysis/BaseTokenStreamTestCase.html">BaseTokenStreamTestCase</a>. By extending
 *     this class, you can create JUnit tests that validate that your
 *     Analyzer and/or analysis components correctly implement the
 *     protocol. The checkRandomData methods of that class are particularly effective in flushing out errors.
 * </p>
 * <h3>Using the TokenStream API</h3>
 * 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.
 * <ol><li>
 * 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.
 * </li>
 * <li>
 * 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.
 * </li>
 * <li>
 * 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.
 * </li>
 * <li>
 * 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 <b>type</b> (<code>Class</code>)
 * of an Attribute as an argument and returns an <b>instance</b>. 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.
 * </li></ol>
 * <h3>Example</h3>
 * <p>
 *   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.
 * </p>
 * <p>
 *   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.
 * </p>
 * <h4>Whitespace tokenization</h4>
 * <pre class="prettyprint">
 * public class MyAnalyzer extends Analyzer {
 * 
 *   private Version matchVersion;
 *   
 *   public MyAnalyzer(Version matchVersion) {
 *     this.matchVersion = matchVersion;
 *   }
 * 
 *   {@literal @Override}
 *   protected TokenStreamComponents createComponents(String fieldName) {
 *     return new TokenStreamComponents(new WhitespaceTokenizer(matchVersion));
 *   }
 *   
 *   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();
 *     }
 *   }
 * }
 * </pre>
 * 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:
 * <pre>
 * This
 * is
 * a
 * demo
 * of
 * the
 * new
 * TokenStream
 * API
 * </pre>
 * <h4>Adding a LengthFilter</h4>
 * 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
 * <code>createComponents()</code> method in our analyzer needs to be changed:
 * <pre class="prettyprint">
 *   {@literal @Override}
 *   protected TokenStreamComponents createComponents(String fieldName) {
 *     final Tokenizer source = new WhitespaceTokenizer(matchVersion);
 *     TokenStream result = new LengthFilter(true, source, 3, Integer.MAX_VALUE);
 *     return new TokenStreamComponents(source, result);
 *   }
 * </pre>
 * Note how now only words with 3 or more characters are contained in the output:
 * <pre>
 * This
 * demo
 * the
 * new
 * TokenStream
 * API
 * </pre>
 * Now let's take a look how the LengthFilter is implemented:
 * <pre class="prettyprint">
 * public final class LengthFilter extends FilteringTokenFilter {
 * 
 *   private final int min;
 *   private final int max;
 *   
 *   private final CharTermAttribute termAtt = addAttribute(CharTermAttribute.class);
 * 
 *   &#47;**
 *    * Create a new LengthFilter. This will filter out tokens whose
 *    * CharTermAttribute is either too short
 *    * (&lt; min) or too long (&gt; max).
 *    * {@literal @param} version the Lucene match version
 *    * {@literal @param} in      the TokenStream to consume
 *    * {@literal @param} min     the minimum length
 *    * {@literal @param} max     the maximum length
 *    *&#47;
 *  public LengthFilter(Version version, TokenStream in, int min, int max) {
 *     super(version, in);
 *     this.min = min;
 *     this.max = max;
 *   }
 * 
 *   {@literal @Override}
 *   public boolean accept() {
 *     final int len = termAtt.length();
 *     return (len &gt;= min &amp;&amp; len &lt;= max);
 *   }
 * 
 * }
 * </pre>
 * <p>
 *   In LengthFilter, the CharTermAttribute is added and stored in the instance
 *   variable <code>termAtt</code>.  Remember that there can only be a single
 *   instance of CharTermAttribute in the chain, so in our example the
 *   <code>addAttribute()</code> call in LengthFilter returns the
 *   CharTermAttribute that the WhitespaceTokenizer already added.
 * </p>
 * <p>
 *   The tokens are retrieved from the input stream in FilteringTokenFilter's 
 *   <code>incrementToken()</code> method (see below), which calls LengthFilter's
 *   <code>accept()</code> 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
 *   <code>accept()</code> can efficiently access the instance variable; no 
 *   attribute lookup is necessary. The same is true for the consumer, which can
 *   simply use local references to the Attributes.
 * </p>
 * <p>
 *   LengthFilter extends FilteringTokenFilter:
 * </p>
 * 
 * <pre class="prettyprint">
 * public abstract class FilteringTokenFilter extends TokenFilter {
 * 
 *   private final PositionIncrementAttribute posIncrAtt = addAttribute(PositionIncrementAttribute.class);
 * 
 *   &#47;**
 *    * Create a new FilteringTokenFilter.
 *    * {@literal @param} in      the TokenStream to consume
 *    *&#47;
 *   public FilteringTokenFilter(Version version, TokenStream in) {
 *     super(in);
 *   }
 * 
 *   &#47;** Override this method and return if the current input token should be returned by incrementToken. *&#47;
 *   protected abstract boolean accept() throws IOException;
 * 
 *   {@literal @Override}
 *   public final boolean incrementToken() throws IOException {
 *     int skippedPositions = 0;
 *     while (input.incrementToken()) {
 *       if (accept()) {
 *         if (skippedPositions != 0) {
 *           posIncrAtt.setPositionIncrement(posIncrAtt.getPositionIncrement() + skippedPositions);
 *         }
 *         return true;
 *       }
 *       skippedPositions += posIncrAtt.getPositionIncrement();
 *     }
 *     // reached EOS -- return false
 *     return false;
 *   }
 * 
 *   {@literal @Override}
 *   public void reset() throws IOException {
 *     super.reset();
 *   }
 * 
 * }
 * </pre>
 * 
 * <h4>Adding a custom Attribute</h4>
 * Now we're going to implement our own custom Attribute for part-of-speech tagging and call it consequently 
 * <code>PartOfSpeechAttribute</code>. First we need to define the interface of the new Attribute:
 * <pre class="prettyprint">
 *   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();
 *   }
 * </pre>
 * <p>
 *   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 <code>PartOfSpeechAttributeImpl</code>.
 * </p>
 * <p>
 *   This should be the usual behavior. However, there is also an expert-API that allows changing these naming conventions:
 *   {@link org.apache.lucene.util.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.
 * </p>
 * <p>
 *   Now here is the actual class that implements our new Attribute. Notice that the class has to extend
 *   {@link org.apache.lucene.util.AttributeImpl}:
 * </p>
 * <pre class="prettyprint">
 * 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;
 *   }
 * 
 *   {@literal @Override}
 *   public void clear() {
 *     pos = PartOfSpeech.Unknown;
 *   }
 * 
 *   {@literal @Override}
 *   public void copyTo(AttributeImpl target) {
 *     ((PartOfSpeechAttribute) target).setPartOfSpeech(pos);
 *   }
 * }
 * </pre>
 * <p>
 *   This is a simple Attribute implementation has only a single variable that
 *   stores the part-of-speech of a token. It extends the
 *   <code>AttributeImpl</code> class and therefore implements its abstract methods
 *   <code>clear()</code> and <code>copyTo()</code>. 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'.
 * </p>
 * <pre class="prettyprint">
 *   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 &gt; 0 &amp;&amp; Character.isUpperCase(term[0])) {
 *         return PartOfSpeech.Noun;
 *       }
 *       return PartOfSpeech.Unknown;
 *     }
 *   }
 * </pre>
 * <p>
 *   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.
 * </p>
 * <p>Now we need to add the filter to the chain in MyAnalyzer:</p>
 * <pre class="prettyprint">
 *   {@literal @Override}
 *   protected TokenStreamComponents createComponents(String fieldName) {
 *     final Tokenizer source = new WhitespaceTokenizer(matchVersion);
 *     TokenStream result = new LengthFilter(true, source, 3, Integer.MAX_VALUE);
 *     result = new PartOfSpeechTaggingFilter(result);
 *     return new TokenStreamComponents(source, result);
 *   }
 * </pre>
 * Now let's look at the output:
 * <pre>
 * This
 * demo
 * the
 * new
 * TokenStream
 * API
 * </pre>
 * 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:
 * <pre class="prettyprint">
 *   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();
 *     }
 *   }
 * </pre>
 * 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:
 * <pre>
 * This: Noun
 * demo: Unknown
 * the: Unknown
 * new: Unknown
 * TokenStream: Noun
 * API: Noun
 * </pre>
 * 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 exercise. 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:
 * <pre class="prettyprint">
 *   public class FirstTokenOfSentenceAttributeImpl extends AttributeImpl
 *                               implements FirstTokenOfSentenceAttribute {
 *     
 *     private boolean firstToken;
 *     
 *     public void setFirstToken(boolean firstToken) {
 *       this.firstToken = firstToken;
 *     }
 *     
 *     public boolean getFirstToken() {
 *       return firstToken;
 *     }
 * 
 *     {@literal @Override}
 *     public void clear() {
 *       firstToken = false;
 *     }
 * 
 *   ...
 * </pre>
 * <h4>Adding a CharFilter chain</h4>
 * Analyzers take Java {@link java.io.Reader}s as input. Of course you can wrap your Readers with {@link java.io.FilterReader}s
 * to manipulate content, but this would have the big disadvantage that character offsets might be inconsistent with your original
 * text.
 * <p>
 * {@link org.apache.lucene.analysis.CharFilter} is designed to allow you to pre-process input like a FilterReader would, but also
 * preserve the original offsets associated with those characters. This way mechanisms like highlighting still work correctly.
 * CharFilters can be chained.
 * <p>
 * Example:
 * <pre class="prettyprint">
 * public class MyAnalyzer extends Analyzer {
 * 
 *   {@literal @Override}
 *   protected TokenStreamComponents createComponents(String fieldName) {
 *     return new TokenStreamComponents(new MyTokenizer());
 *   }
 *   
 *   {@literal @Override}
 *   protected Reader initReader(String fieldName, Reader reader) {
 *     // wrap the Reader in a CharFilter chain.
 *     return new SecondCharFilter(new FirstCharFilter(reader));
 *   }
 * }
 * </pre>
 */
package org.apache.lucene.analysis;
`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.
`PositionLengthAttribute`	The number of positions occupied by a token.
`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.
Term	IBM	International	Business	Machines
Position increment	1	0	1	1
Position length	3	1	1	1
/

org.apache.lucene/ lucene-core/ 8.7.0/ org/apache/lucene/analysis/package-info.java

Parsing? Tokenization? Analysis!

Parsing

Tokenization

Core Analysis

Hints, Tips and Traps

Invoking the Analyzer

Indexing Analysis vs. Search Analysis

Implementing your own Analyzer and Analysis Components

Field Section Boundaries

End of Input Cleanup

Token Position Increments

Token Position Length

How to not write corrupt token streams

TokenStream API

Attribute and AttributeSource

More Requirements for Analysis Component Classes

Token Stream Lifetime

Tokenizer

Token Filter

Creating delegates

Testing Your Analysis Component

Using the TokenStream API

Example

Whitespace tokenization

Adding a LengthFilter

Adding a custom Attribute

Adding a CharFilter chain
