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

Kin Man POON, Chun Fai LEUNG, Peixian CHEN, Nevin L. ZHANG

Research output: Chapter in Book/Report/Conference proceedingChapter

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. Copyright © 2017 Springer International Publishing AG.
Original languageEnglish
Title of host publicationWeb and big data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings
EditorsLei CHEN, Christian S. JENSEN, Cyrus SHAHABI, Xiaochun YANG, Xiang LIAN
Place of PublicationCham
PublisherSpringer
Pages341-344
ISBN (Electronic)9783319635644
ISBN (Print)9783319635637
DOIs
Publication statusPublished - 2017

Fingerprint

Statistical Models

Citation

Poon, L. K. M., Leung, C. F., Chen, P., & Zhang, N. L. (2017). Topic browsing system for research papers based on hierarchical latent tree analysis. In L. Chen, C. S. Jensen, C. Shahabi, X. Yang, & X. Lian (Eds.), Web and big data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings (pp. 341-344). Cham: Springer.