# Package org.apache.lucene.search.highlight

package org.apache.lucene.search.highlight
Highlighting search terms.

The highlight package contains classes to provide "keyword in context" features typically used to highlight search terms in the text of results pages. The Highlighter class is the central component and can be used to extract the most interesting sections of a piece of text and highlight them, with the help of Fragmenter, fragment Scorer, and Formatter classes.

## Example Usage

     //... Above, create documents with two fields, one with term vectors (tv) and one without (notv)
     IndexSearcher searcher = new IndexSearcher(directory);
     QueryParser parser = new QueryParser("notv", analyzer);
     Query query = parser.parse("million");

       TopDocs hits = searcher.search(query, 10);

       SimpleHTMLFormatter htmlFormatter = new SimpleHTMLFormatter();
       Highlighter highlighter = new Highlighter(htmlFormatter, new QueryScorer(query));
       for (int i = 0; i < 10; i++) {
         int id = hits.scoreDocs[i].doc;
         Document doc = searcher.doc(id);
         String text = doc.get("notv");
         TokenStream tokenStream = TokenSources.getAnyTokenStream(searcher.getIndexReader(), id, "notv", analyzer);
         TextFragment[] frag = highlighter.getBestTextFragments(tokenStream, text, false, 10);//highlighter.getBestFragments(tokenStream, text, 3, "...");
         for (int j = 0; j < frag.length; j++) {
           if ((frag[j] != null) && (frag[j].getScore() > 0)) {
             System.out.println((frag[j].toString()));
           }
         }
         //Term vector
         text = doc.get("tv");
         tokenStream = TokenSources.getAnyTokenStream(searcher.getIndexReader(), hits.scoreDocs[i].doc, "tv", analyzer);
         frag = highlighter.getBestTextFragments(tokenStream, text, false, 10);
         for (int j = 0; j < frag.length; j++) {
           if ((frag[j] != null) && (frag[j].getScore() > 0)) {
             System.out.println((frag[j].toString()));
           }
         }
         System.out.println("-------------");
       }

## New features 06/02/2005

This release adds options for encoding (thanks to Nicko Cadell). An "Encoder" implementation such as the new SimpleHTMLEncoder class can be passed to the highlighter to encode all those non-xhtml standard characters such as & into legal values. This simple class may not suffice for some languages - Commons Lang has an implementation that could be used: escapeHtml(String) in http://svn.apache.org/viewcvs.cgi/jakarta/commons/proper/lang/trunk/src/java/org/apache/commons/lang/StringEscapeUtils.java?rev=137958&view=markup

## New features 22/12/2004

This release adds some new capabilities:

1. Faster highlighting using Term vector support
2. New formatting options to use color intensity to show informational value
3. Options for better summarization by using term IDF scores to influence fragment selection

The highlighter takes a TokenStream as input. Until now these streams have typically been produced using an Analyzer but the new class TokenSources provides helper methods for obtaining TokenStreams from the new TermVector position support (see latest CVS version).

The new class GradientFormatter can use a scale of colors to highlight terms according to their score. A subtle use of color can help emphasise the reasons for matching (useful when doing "MoreLikeThis" queries and you want to see what the basis of the similarities are).

The QueryScorer class has constructors that use an IndexReader to derive the IDF (inverse document frequency) for each term in order to influence the score. This is useful for helping to extracting the most significant sections of a document and in supplying scores used by the new GradientFormatter to color significant words more strongly. The QueryScorer.getMaxTermWeight method is useful when passed to the GradientFormatter constructor to define the top score which is associated with the top color.

- Class
  Description
  DefaultEncoder
  Simple `Encoder` implementation that does not modify the output
  Encoder
  Encodes original text.
  Formatter
  Processes terms found in the original text, typically by applying some form of mark-up to highlight terms in HTML search results pages.
  Fragmenter
  Implements the policy for breaking text into multiple fragments for consideration by the `Highlighter` class.
  GradientFormatter
  Formats text with different color intensity depending on the score of the term.
  Highlighter
  Marks up highlighted terms found in the best sections of text, using configurable `Fragmenter`, `Scorer`, `Formatter`, `Encoder` and tokenizers.
  InvalidTokenOffsetsException
  Exception thrown if TokenStream Tokens are incompatible with provided text
  NullFragmenter
  `Fragmenter` implementation which does not fragment the text.
  OffsetLimitTokenFilter
  This TokenFilter limits the number of tokens while indexing by adding up the current offset.
  PositionSpan
  Utility class to record Positions Spans
  QueryScorer
  `Scorer` implementation which scores text fragments by the number of unique query terms found.
  QueryTermExtractor
  Utility class used to extract the terms used in a query, plus any weights.
  QueryTermScorer
  `Scorer` implementation which scores text fragments by the number of unique query terms found.
  Scorer
  A Scorer is responsible for scoring a stream of tokens.
  SimpleFragmenter
  `Fragmenter` implementation which breaks text up into same-size fragments with no concerns over spotting sentence boundaries.
  SimpleHTMLEncoder
  Simple `Encoder` implementation to escape text for HTML output
  SimpleHTMLFormatter
  Simple `Formatter` implementation to highlight terms with a pre and post tag.
  SimpleSpanFragmenter
  `Fragmenter` implementation which breaks text up into same-size fragments but does not split up `Spans`.
  SpanGradientFormatter
  Formats text with different color intensity depending on the score of the term using the span tag.
  TermVectorLeafReader
  Wraps a Terms with a `LeafReader`, typically from term vectors.
  TextFragment
  Low-level class used to record information about a section of a document with a score.
  TokenGroup
  One, or several overlapping tokens, along with the score(s) and the scope of the original text.
  TokenSources
  Convenience methods for obtaining a `TokenStream` for use with the `Highlighter` - can obtain from term vectors with offsets and positions or from an Analyzer re-parsing the stored content.
  TokenStreamFromTermVector
  TokenStream created from a term vector field.
  WeightedSpanTerm
  Lightweight class to hold term, weight, and positions used for scoring this term.
  WeightedSpanTermExtractor
  Class used to extract `WeightedSpanTerm`s from a `Query` based on whether `Term`s from the `Query` are contained in a supplied `TokenStream`.
  WeightedSpanTermExtractor.PositionCheckingMap<K>
  This class makes sure that if both position sensitive and insensitive versions of the same term are added, the position insensitive one wins.
  WeightedTerm
  Lightweight class to hold term and a weight value used for scoring this term