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    A Phrase Topic Model Based on Distributed Representation

    Jialin Ma1, *, Jieyi Cheng1, Lin Zhang1, Lei Zhou1, Bolun Chen1, 2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 455-469, 2020, DOI:10.32604/cmc.2020.09780

    Abstract Traditional topic models have been widely used for analyzing semantic topics from electronic documents. However, the obvious defects of topic words acquired by them are poor in readability and consistency. Only the domain experts are possible to guess their meaning. In fact, phrases are the main unit for people to express semantics. This paper presents a Distributed Representation-Phrase Latent Dirichlet Allocation (DRPhrase LDA) which is a phrase topic model. Specifically, we reasonably enhance the semantic information of phrases via distributed representation in this model. The experimental results show the topics quality acquired by our model is more readable and consistent… More >

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