+++ /dev/null
-<!doctype html public "-//w3c//dtd html 4.0 transitional//en">
-<!--
- Licensed to the Apache Software Foundation (ASF) under one or more
- contributor license agreements. See the NOTICE file distributed with
- this work for additional information regarding copyright ownership.
- The ASF licenses this file to You under the Apache License, Version 2.0
- (the "License"); you may not use this file except in compliance with
- the License. You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
--->
-<html>
-<body>
-
-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.
-
-<h2>Example Usage</h2>
-
-<pre class="prettyprint">
- //... 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("-------------");
- }
-</pre>
-
-<h2>New features 06/02/2005</h2>
-
-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
-
-<h2>New features 22/12/2004</h2>
-
-This release adds some new capabilities:
-<ol>
- <li>Faster highlighting using Term vector support</li>
- <li>New formatting options to use color intensity to show informational value</li>
- <li>Options for better summarization by using term IDF scores to influence fragment selection</li>
-</ol>
-
-<p>
-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).</p>
-
-<p>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).</p>
-
-<p>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 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.getMaxWeight method is useful when passed to the GradientFormatter constructor to define the top score
-which is associated with the top color.</p>
-
-
-
-
-</body>
-</html>
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fnp / pylucene.git / blobdiff