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我目前正在尝试为 lucene 索引中的术语计算 tf-idf 矩阵。我尝试使用以下功能来做到这一点：

public Table<Integer, BytesRef, Double> tfidf(String field) throws IOException, ParseException{
    //variables in complete context
    int totalNoOfDocs = reader.numDocs();                                   //total no of docs
    HashBasedTable<Integer, BytesRef, Double> tfidfPerDocAndTerm = HashBasedTable.create(); //tfidf value for each document(integer) and term(Byteref) pair.

    //variables in loop context
    BytesRef    term;                                                       //term as BytesRef
    int         noOfDocs;                                                   //number of documents (a term occours in)
    int         tf;                                                         //term frequency (of a term in a doc)
    double      idf;                                                        //inverse document frequency (of a term in a doc)
    double      tfidf;                                                      //term frequency - inverse document frequency value (of a term in a doc)
    Terms       termVector;                                                 //all terms of current doc in current field
    TermsEnum   termsEnum;                                                  //iterator for terms
    DocsEnum    docsEnum;                                                   //iterator for documents (of current term)

    List<Integer> docIds = getDocIds(totalNoOfDocs);                        //get internal documentIds of documents

    try {
        for(int doc : docIds){
            termVector  = reader.getTermVector(doc, field);                 //get termvector for document
            termsEnum   = termVector.iterator(null);                        //get iterator of termvector to iterate over terms


            while((term = termsEnum.next()) != null){                       //iterate of terms

                    noOfDocs = termsEnum.docFreq();                         //add no of docs the term occurs in to list

                    docsEnum = termsEnum.docs(null, null);                  //get document iterator for this term (all documents the term occours in)
                    while((doc = docsEnum.nextDoc()) != DocIdSetIterator.NO_MORE_DOCS){ //iterate over documents - computation of all tf-idf values for this term
                        tf      = docsEnum.freq();                          //get termfrequency of current term in current doc
                        idf     = Math.log((double)totalNoOfDocs / (double)noOfDocs); //calculate idf
                        tfidf   = (double) tf * idf;                        //caculate tfidf
                        tfidfPerDocAndTerm.put(doc, term, tfidf);           //add tf-idf value to matrix

                    }
            }
        }

    } catch (IOException ex) {
        Logger.getLogger(Index.class.getName()).log(Level.SEVERE, null, ex);
    }   
    return tfidfPerDocAndTerm;
}

问题是： noOfDocs = termsEnum.docFreq(); 总是返回 1。即使显然存在多个文档中的术语（通过打印“术语”手动检查）。

我还发现，我检索的 docsEnum 是： docsEnum = termsEnum.docs(null, null); 总是只包含 1 个文档（doc 0）。

在创建索引时，我使用了带有停用词列表的标准分析器，因此所有术语都是小写的。

那我的问题是什么？：/

感谢您的帮助！

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2 回答 2

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实际上，您的术语（属于 BytesRef 类型）是循环的，而不是您的术语集，但不幸的是，BytesRef 不支持称为 freq() 或 docfreq() 的方法

于 2013-10-30T06:19:57.067 回答
0

实际上，枚举器总是返回 1。但是您可以使用以下方法获得正确的值CollectionStatistics：

DefaultSimilarity similarity = new DefaultSimilarity();

IndexReader reader = searcher.getIndexReader();
IndexReaderContext context = searcher.getTopReaderContext();
CollectionStatistics collectionStats = searcher.collectionStatistics(FIELD);
long totalDocCount = collectionStats.docCount();

Terms termVector = reader.getTermVector(docId, FIELD);
TermsEnum iterator = termVector.iterator(null);

while (true) {
    BytesRef ref = iterator.next();
    if (ref == null) {
        break;
    }

    long termFreq = iterator.totalTermFreq();
    float tf = similarity.tf(termFreq);

    Term term = new Term(FIELD, ref);
    TermContext termContext = TermContext.build(context, term);

    TermStatistics termStats = searcher.termStatistics(term, termContext);
    long docFreq = termStats.docFreq();
    float idf = similarity.idf(docFreq, totalDocCount);

    // do something with tf and idf
}

请注意，要使其正常工作，您需要将术语向量存储在索引中。

于 2016-03-14T16:57:20.133 回答