Document topic mining method and apparatus

Abstract

The present application proposes a document topic mining method and apparatus. The method comprises: according to a preset topic mining number, performing loop iteration processing on information in at least one received document based on a probabilistic latent semantic analysis model, and acquiring a posteriori estimate of each topic implied by each sentence in each document; according to the posteriori estimate of each topic, acquiring a membership weight of each word in each topic in each sentence; and generating a topic set corresponding to the topic mining number, wherein each topic set comprises a word related to each topic and screened out according to the membership weight of each word in each topic in the sentence. According to the document topic mining method and apparatus provided by the present application, the document topic is more comprehensively and accurately mined based on a PLSA (Probabilistic Latent Semantic Analysis) algorithm, and the correlation of document topic content is improved, thereby enabling a result of a search engine to be closer to semantic information of the document.

Description

technical field [0001] The present application relates to the technical field of information processing, and in particular to a document topic mining method and device. Background technique [0002] At present, the way people obtain information on the Internet is mainly through search engines. The results of traditional document retrieval largely depend on the literal matching of documents, and cannot handle the hidden semantic information of documents well. [0003] Therefore, in the prior art, topic models are used to perform semantic mining on documents, and a commonly used topic model algorithm is PLSA (Probabilistic Latent Semantic Analysis, Probabilistic Latent Semantic Analysis) algorithm. Based on the topic model algorithm, the search engine can automatically obtain the topic distribution behind the document, so that the results of the search engine are closer to the semantic information of the document, thereby reducing the cost for users to obtain information. It ...

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Problems solved by technology

[0004] However, the above-mentioned process of PLSA’s semantic mining of documents only considers the relevance of words appearing in the context, and uses multinomial distributions on the vocabulary to represent hidden topics. Therefore, the current PLSA algorithm for document topic mining The process is not comprehensive and precise enough, and the relevance of the subject content of the document is poor

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