问题描述
使用 Lucene,在搜索结果中定位匹配项的推荐方法是什么?
With Lucene, what would be the recommended approach for locating matches in search results?
更具体地说,假设索引文档有一个字段fullText",用于存储某个文档的纯文本内容.此外,假设这些文档之一的内容是敏捷的棕色狐狸跳过懒惰的狗".接下来对fox dog"执行搜索.显然,该文件将大受欢迎.
More specifically, suppose index documents have a field "fullText" which stores the plain-text content of some document. Furthermore, assume that for one of these documents the content is "The quick brown fox jumps over the lazy dog". Next a search is performed for "fox dog". Obviously, the document would be a hit.
在这种情况下,可以使用 Lucene 来为找到的文档提供匹配区域吗?因此,对于这种情况,我想生成如下内容:
In this scenario, can Lucene be used to provide something like the matching regions for found document? So for this scenario I would like to produce something like:
[{match: "fox", startIndex: 10, length: 3},
{match: "dog", startIndex: 34, length: 3}]
我怀疑它可以通过 org.apache.lucene.search.highlight 包中提供的内容来实现.我不确定整体方法虽然...
I suspect that it could be implemented by what's provided in the org.apache.lucene.search.highlight package. I'm not sure about the overall approach though...
推荐答案
TermFreqVector 是我使用的.这是一个工作演示,它打印术语位置以及开始和结束术语索引:
TermFreqVector is what I used. Here is a working demo, that prints both the term positions, and the starting and ending term indexes:
public class Search {
public static void main(String[] args) throws IOException, ParseException {
Search s = new Search();
s.doSearch(args[0], args[1]);
}
Search() {
}
public void doSearch(String db, String querystr) throws IOException, ParseException {
// 1. Specify the analyzer for tokenizing text.
// The same analyzer should be used as was used for indexing
StandardAnalyzer analyzer = new StandardAnalyzer(Version.LUCENE_CURRENT);
Directory index = FSDirectory.open(new File(db));
// 2. query
Query q = new QueryParser(Version.LUCENE_CURRENT, "contents", analyzer).parse(querystr);
// 3. search
int hitsPerPage = 10;
IndexSearcher searcher = new IndexSearcher(index, true);
IndexReader reader = IndexReader.open(index, true);
searcher.setDefaultFieldSortScoring(true, false);
TopScoreDocCollector collector = TopScoreDocCollector.create(hitsPerPage, true);
searcher.search(q, collector);
ScoreDoc[] hits = collector.topDocs().scoreDocs;
// 4. display term positions, and term indexes
System.out.println("Found " + hits.length + " hits.");
for(int i=0;i<hits.length;++i) {
int docId = hits[i].doc;
TermFreqVector tfvector = reader.getTermFreqVector(docId, "contents");
TermPositionVector tpvector = (TermPositionVector)tfvector;
// this part works only if there is one term in the query string,
// otherwise you will have to iterate this section over the query terms.
int termidx = tfvector.indexOf(querystr);
int[] termposx = tpvector.getTermPositions(termidx);
TermVectorOffsetInfo[] tvoffsetinfo = tpvector.getOffsets(termidx);
for (int j=0;j<termposx.length;j++) {
System.out.println("termpos : "+termposx[j]);
}
for (int j=0;j<tvoffsetinfo.length;j++) {
int offsetStart = tvoffsetinfo[j].getStartOffset();
int offsetEnd = tvoffsetinfo[j].getEndOffset();
System.out.println("offsets : "+offsetStart+" "+offsetEnd);
}
// print some info about where the hit was found...
Document d = searcher.doc(docId);
System.out.println((i + 1) + ". " + d.get("path"));
}
// searcher can only be closed when there
// is no need to access the documents any more.
searcher.close();
}
}
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