See: Description
Interface | Description |
---|---|
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. |
Scorer |
Adds to the score for a fragment based on its tokens
|
Class | Description |
---|---|
DefaultEncoder |
Simple
Encoder implementation that does not modify the output |
GradientFormatter |
Formats text with different color intensity depending on the score of the
term.
|
Highlighter |
Class used to markup highlighted terms found in the best sections of a
text, using configurable
Fragmenter , Scorer , Formatter ,
Encoder and tokenizers. |
NullFragmenter |
Fragmenter implementation which does not fragment the text. |
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.
|
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.
|
SpanScorer |
Scorer implementation which scores text fragments by the number of
unique query terms found. |
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 |
Hides implementation issues associated with obtaining a TokenStream for use with
the higlighter - can obtain from TermFreqVectors with offsets and (optionally) positions or
from Analyzer class reparsing the stored content.
|
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 Terms from the query are contained in a supplied TokenStream. |
WeightedTerm |
Lightweight class to hold term and a weight value used for scoring this term
|
//... 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"); //query = query.rewrite(reader); //required to expand search terms Hits hits = searcher.search(query); SimpleHTMLFormatter htmlFormatter = new SimpleHTMLFormatter(); Highlighter highlighter = new Highlighter(htmlFormatter, new QueryScorer(query)); for (int i = 0; i < 10; i++) { String text = hits.doc(i).get("notv"); TokenStream tokenStream = TokenSources.getAnyTokenStream(searcher.getIndexReader(), hits.id(i), "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 = hits.doc(i).get("tv"); tokenStream = TokenSources.getAnyTokenStream(searcher.getIndexReader(), hits.id(i), "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("-------------"); }
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 a new constructor which can use an IndexReader to derive the IDF (inverse document frequency) for each term in order to influcence 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.getMaxWeight method is useful when passed to the GradientFormatter constructor to define the top score which is associated with the top color.
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