@Article{cmc.2020.09780,
AUTHOR = {Jialin Ma, Jieyi Cheng, Lin Zhang, Lei Zhou, Bolun Chen},
TITLE = {A Phrase Topic Model Based on Distributed Representation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {455--469},
URL = {http://www.techscience.com/cmc/v64n1/39152},
ISSN = {1546-2226},
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 than other similar topic models.},
DOI = {10.32604/cmc.2020.09780}
}