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A Phrase Topic Model Based on Distributed Representation

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

1 Jiangsu Internet of Things and Moblie Internet Technology Engineering Laboratory, Huaiyin Institute of Technology, Huai’an, 223003, China.
2 University of Fribourg, Fribourg, 1700, Switzerland.

* Corresponding Author: Jialin Ma. Email: email.

Computers, Materials & Continua 2020, 64(1), 455-469.


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 than other similar topic models.


Cite This Article

J. Ma, J. Cheng, L. Zhang, L. Zhou and B. Chen, "A phrase topic model based on distributed representation," Computers, Materials & Continua, vol. 64, no.1, pp. 455–469, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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