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.
|Publication status||Published - Jul 2017|