使用Lucene 7 OpenNLP查询词性标签

Question

为了好玩和学习，我正在尝试用OpenNLP和Lucene 7.4构建一个词性（POS）标记器。目标是，一旦建立索引，我就可以搜索一系列的词性标签，并找到所有符合序列的句子。我已经得到了索引部分，但我被困在查询部分。我知道SolR可能有一些这方面的功能，我已经检查了代码（这毕竟不是自我解释的）。但我的目标是在Lucene 7中理解和实现，而不是在SolR中，因为我想独立于任何顶级搜索引擎。

想法输入句子1：敏捷的棕色狐狸跳过了懒惰的狗。Applied Lucene OpenNLP tokenizer的结果是：[The][quick][brown][fox][hipped][over][The][lazy][dogs][.]接下来，在[DT][JJ][JJ][NN][VBD][in][DT][JJ][NNS][中应用Lucene OpenNLP词性标注结果

输入句子2：给我，宝贝！Applied Lucene OpenNLP tokenizer的结果是：[Give][it][to][me][，][baby][！]接下来，在[VB][PRP][TO][PRP][，][UH][中应用Lucene OpenNLP词性标注结果

查询：JJ NN VBD匹配句子1的一部分，所以应该返回句子1。（此时，我只对精确匹配感兴趣，也就是说，让我们撇开部分匹配、通配符等。）

首先，我创建了自己的类com。实例OpenNLPAnalyzer：

public class OpenNLPAnalyzer extends Analyzer {
  protected TokenStreamComponents createComponents(String fieldName) {
    try {

        ResourceLoader resourceLoader = new ClasspathResourceLoader(ClassLoader.getSystemClassLoader());


        TokenizerModel tokenizerModel = OpenNLPOpsFactory.getTokenizerModel("en-token.bin", resourceLoader);
        NLPTokenizerOp tokenizerOp = new NLPTokenizerOp(tokenizerModel);


        SentenceModel sentenceModel = OpenNLPOpsFactory.getSentenceModel("en-sent.bin", resourceLoader);
        NLPSentenceDetectorOp sentenceDetectorOp = new NLPSentenceDetectorOp(sentenceModel);

        Tokenizer source = new OpenNLPTokenizer(
                AttributeFactory.DEFAULT_ATTRIBUTE_FACTORY, sentenceDetectorOp, tokenizerOp);

        POSModel posModel = OpenNLPOpsFactory.getPOSTaggerModel("en-pos-maxent.bin", resourceLoader);
        NLPPOSTaggerOp posTaggerOp = new NLPPOSTaggerOp(posModel);

        // Perhaps we should also use a lower-case filter here?

        TokenFilter posFilter = new OpenNLPPOSFilter(source, posTaggerOp);

        // Very important: Tokens are not indexed, we need a store them as payloads otherwise we cannot search on them
        TypeAsPayloadTokenFilter payloadFilter = new TypeAsPayloadTokenFilter(posFilter);

        return new TokenStreamComponents(source, payloadFilter);
    }
    catch (IOException e) {
        throw new RuntimeException(e.getMessage());
    }              

}

请注意，我们使用的是围绕OpenNLPPOSFilter的TypeAsPayloadTokenFilter。这意味着，我们的POS标签将被索引为有效负载，我们的查询——不管它看起来如何——也必须搜索有效负载。

查询这就是我陷入困境的地方。我不知道如何查询有效负载，无论我尝试什么都不起作用。请注意，我使用的是Lucene 7，似乎在旧版本中查询有效负载已经更改了几次。文档极其稀缺。现在甚至不清楚要查询的正确字段名称是什么——是“word”还是“type”还是其他任何东西？例如，我尝试了这段不返回任何搜索结果的代码：

    // Step 1: Indexing
    final String body = "The quick brown fox jumped over the lazy dogs.";
    Directory index = new RAMDirectory();
    OpenNLPAnalyzer analyzer = new OpenNLPAnalyzer();
    IndexWriterConfig indexWriterConfig = new IndexWriterConfig(analyzer);
    IndexWriter writer = new IndexWriter(index, indexWriterConfig);
    Document document = new Document();
    document.add(new TextField("body", body, Field.Store.YES));
    writer.addDocument(document);
    writer.close();


    // Step 2: Querying
    final int topN = 10;
    DirectoryReader reader = DirectoryReader.open(index);
    IndexSearcher searcher = new IndexSearcher(reader);

    final String fieldName = "body"; // What is the correct field name here? "body", or "type", or "word" or anything else?
    final String queryText = "JJ";
    Term term = new Term(fieldName, queryText);
    SpanQuery match = new SpanTermQuery(term);
    BytesRef pay = new BytesRef("type"); // Don't understand what to put here as an argument
    SpanPayloadCheckQuery query = new SpanPayloadCheckQuery(match, Collections.singletonList(pay));

    System.out.println(query.toString());

    TopDocs topDocs = searcher.search(query, topN);

非常感谢您的帮助。

Answer 1

为什么不使用TypeAsSynonymFilter而不是TypeAsPayloadTokenFilter，只做一个普通的查询呢。所以在你的分析器中：

:
TokenFilter posFilter = new OpenNLPPOSFilter(source, posTaggerOp);
TypeAsSynonymFilter typeAsSynonymFilter = new TypeAsSynonymFilter(posFilter);
return new TokenStreamComponents(source, typeAsSynonymFilter);

和索引方面：

static Directory index() throws Exception {
  Directory index = new RAMDirectory();
  OpenNLPAnalyzer analyzer = new OpenNLPAnalyzer();
  IndexWriterConfig indexWriterConfig = new IndexWriterConfig(analyzer);
  IndexWriter writer = new IndexWriter(index, indexWriterConfig);
  writer.addDocument(doc("The quick brown fox jumped over the lazy dogs."));
  writer.addDocument(doc("Give it to me, baby!"));
  writer.close();

  return index;
}

static Document doc(String body){
  Document document = new Document();
  document.add(new TextField(FIELD, body, Field.Store.YES));
  return document;
}

搜索端：

static void search(Directory index, String searchPhrase) throws Exception {
  final int topN = 10;
  DirectoryReader reader = DirectoryReader.open(index);
  IndexSearcher searcher = new IndexSearcher(reader);

  QueryParser parser = new QueryParser(FIELD, new WhitespaceAnalyzer());
  Query query = parser.parse(searchPhrase);
  System.out.println(query);

  TopDocs topDocs = searcher.search(query, topN);
  System.out.printf("%s => %d hits\n", searchPhrase, topDocs.totalHits);
  for(ScoreDoc scoreDoc: topDocs.scoreDocs){
    Document doc = searcher.doc(scoreDoc.doc);
    System.out.printf("\t%s\n", doc.get(FIELD));
  }
}

然后像这样使用它们：

public static void main(String[] args) throws Exception {
  Directory index = index();
  search(index, "\"JJ NN VBD\"");    // search the sequence of POS tags
  search(index, "\"brown fox\"");    // search a phrase
  search(index, "\"fox brown\"");    // search a phrase (no hits)
  search(index, "baby");             // search a word
  search(index, "\"TO PRP\"");       // search the sequence of POS tags
}

结果如下：

body:"JJ NN VBD"
"JJ NN VBD" => 1 hits
    The quick brown fox jumped over the lazy dogs.
body:"brown fox"
"brown fox" => 1 hits
    The quick brown fox jumped over the lazy dogs.
body:"fox brown"
"fox brown" => 0 hits
body:baby
baby => 1 hits
    Give it to me, baby!
body:"TO PRP"
"TO PRP" => 1 hits
    Give it to me, baby!