Python Gensim:如何使用LDA模型计算文档相似度?

Don't know if this'll help but, I managed to attain successful results on document matching and similarities when using the actual document as a query.dictionary = corpora.Dictionary.load('dictionary.dict')corpus = corpora.MmCorpus("corpus.mm")lda = models.LdaModel.load("model.lda") #result from running online lda (training)index = similarities.MatrixSimilarity(lda[corpus])index.save("simIndex.index")docname = "docs/the_doc.txt"doc = open(docname, 'r').read()vec_bow = dictionary.doc2bow(doc.lower().split())vec_lda = lda[vec_bow]sims = index[vec_lda]sims = sorted(enumerate(sims), key=lambda item: -item[1])print sims您在语料库中驻留的所有文档与用作查询的文档之间的相似性得分将是每个sim卡中sim卡的第二个索引.Your similarity score between all documents residing in the corpus and the document that was used as a query will be the second index of every sim for sims. 这篇关于Python Gensim:如何使用LDA模型计算文档相似度?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！