Generation method and device of topic model and acquisition method and device of topic distribution

Abstract

The invention provides a generation method and a generation device of a topic model and an acquisition method and an acquisition device of topic distribution. On the one hand, a first posterior probability parameter of word pairs in a training sample is obtained, and therefore a candidate expectation of the topic distribution of the word pairs in the training sample is obtained according to the first posterior probability parameter, and on the other hand, a convergence degree of the topic model is obtained according to the candidate expectation of the topic distribution of the word pairs in the training sample, and then if the convergence degree of the topic model meets a terminal condition, the candidate expectation of the topic distribution of the word pairs in the training sample is used as a target expectation of the topic distribution. Each word pair comprises two different word entries in the training sample. Accordingly, the generation method and the generation device of the topic model and the acquisition method and the acquisition device of the topic distribution are used to solve the problems that a method of using a traditional topic model to obtain topic distribution of a text is low in accuracy and stability in the prior art.

Description

【Technical field】 [0001] The present invention relates to the field of computer technology, in particular to a method and device for generating a topic model, and a method and device for obtaining topic distribution. 【Background technique】 [0002] In the field of machine learning and natural language processing, it is often necessary to mine the potential semantic relationship between words in the text domain, that is, the topic, from a large amount of text. Through the learning and prediction of the topic model, the topic distribution of the text can be obtained, which can be used to implement text clustering and be applied to subsequent tasks such as classification, retrieval, expansion, and recommendation. [0003] In the prior art, traditional topic models, such as Probability Latent Semantic Analysis (PLSA) algorithm, Non-negative Matrix Factorization (NMF) algorithm, Latent Dirichlet Allocation (Latent Dirichlet Allocation , LDA) algorithm adopts the concept of bag o...

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Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides a method and device for generating a topic model, and a method and device for obtaining a topic distribution, so as to solve the problem of obtaining the accuracy and stability of the topic distribution of a text using a traditional topic model in the prior art. lower sex problems

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