Latent Dirichlet allocation (LDA) has been widely used for analyzing large text corpora. In this paper we propose the topic-weak-correlated LDA (TWC-LDA) for topic modeling, which constrains different topics to be weak-correlated. This is technically achieved by placing a special prior over the topic-word distributions. Reducing the overlapping between the topic-word distributions makes the learned topics more interpretable in the sense that each topic word-distribution can be clearly associated to a distinctive semantic meaning. Experimental results on both synthetic and real-world corpus show the superiority of the TWC-LDA over the basic LDA for semantically meaningful topic discovery and document classification.
['Yimin Tan', 'Zhijian Ou']
Topic-weak-correlated Latent Dirichlet allocation