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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