  Standard LDA model suffers the problem that the topic assignment of each word
is independent and word correlation hence is neglected. To address this
problem, in this paper, we propose a model called Word Related Latent Dirichlet
Allocation (WR-LDA) by incorporating word correlation into LDA topic models.
This leads to new capabilities that standard LDA model does not have such as
estimating infrequently occurring words or multi-language topic modeling.
Experimental results demonstrate the effectiveness of our model compared with
standard LDA.
