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Topic Discovery based on LDA_col Model and Topic Significance Re-ranking

Author(s): Lidong Wang | Baogang Wei | Jie Yuan

Journal: Journal of Computers
ISSN 1796-203X

Volume: 6;
Issue: 8;
Start page: 1639;
Date: 2011;
Original page

Keywords: Topic model | LDA | Latent Dirichlet Allocation_Collocation | Topic significance re-ranking | Topic Coverage | Topic Similarity

This paper presents a method to find the topics efficiently by the combination of topic discovery and topic re-ranking. Most topic models rely on the bag-of-words(BOW) assumption. Our approach allows an extension of LDA model—Latent Dirichlet Allocation_Collocation (LDA_col) to work in corpus such that the word order can be taken into consideration for phrase discovery, and slightly modify the modal for modal consistency and effectiveness. However, LDA_col results may not be ideal for user’s understanding. In order to improve the topic modeling results, two topic significance re-ranking methods (Topic Coverage(TC) and Topic Similarity(TS)) are proposed. We conduct our method on both English and Chinese corpus, the experimental results show that themodified LDA_col discovers more meaningful phrases and more understandable topics than LDA and LDA_col.Meanwhile, topic re-ranking method based on TC performs better than TS, and has the ability of re-ranking the “significant” topics higher than “insignificant” ones.
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