Topic browsing system for research papers based on hierarchical latent tree analysis

Kin Man POON, Chun Fai LEUNG, Peixian CHEN, Nevin Lianwen ZHANG

Research output: Contribution to conferencePapers

Abstract

New academic papers appear rapidly in the literature nowadays. This poses a challenge for researchers who are trying to keep up with a given field, especially those who are new to a field and may not know where to start from. To address this kind of problems, we have developed a topic browsing system for research papers where the papers have been automatically categorized by a probabilistic topic model. Rather than using Latent Dirichlet Allocation (LDA) for topic modeling, we use a recently proposed method called hierarchical latent tree analysis, which has been shown to perform better than some state-of-the-art LDA-based methods. The resulting topic model contains a hierarchy of topics so that users can browse topics at different levels. The topic model contains a manageable number of general topics at the top level and allows thousands of fine-grained topics at the bottom level.
Original languageEnglish
Publication statusPublished - Jul 2017

Citation

Poon, L. K., Leung, C. F., Chen, P., & Zhang, N. L. (2017, July). Topic browsing system for research papers based on hierarchical latent tree analysis. Paper presented at the 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data (APWeb-WAIM 2017), Beijing Friendship Hotel, Beijing, China.

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