python - 如何获得主题模型的覆盖率？

Question

我已经使用 gensim 运行了 LSI，但我不知道如何获得覆盖率，即描述语料库的主题数 n。

我能够获得主题向量，但我可以使用它吗？它与连贯性或困惑不同吗？

def prepare_corpus(doc_clean):
    """
    Input  : clean document
    Output : term dictionary and Document Term Matrix
    """
    # Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
    tfidf_instance=models.TfidfModel(doc_term_matrix)
    tfidf_corpus=tfidf_instance[doc_term_matrix]
    # generate LDA model
    return dictionary,doc_term_matrix,tfidf_corpus

def create_gensim_lsa_model(doc_clean,number_of_topics):
    """
    Input  : clean document, number of topics and number of words associated with each topic
    Purpose: create LSA model using gensim
    Output : return LSA model
    """
    dictionary,doc_term_matrix, tfidf_corpus =prepare_corpus(doc_clean)
    # generate LSA model
    #lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary)  # train model
    lsamodel_tfidf = LsiModel(tfidf_corpus, num_topics=number_of_topics, id2word = dictionary)
    return lsamodel_tfidf

标签： pythonnlpgensimldalatent-semantic-indexing