Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis
This addresses the challenge for academic researchers and postgraduate students in efficiently exploring literature, though it is incremental as it applies an existing topic modeling method to a new dataset.
The authors tackled the problem of navigating large collections of research papers by developing an online catalog that automatically categorizes 7,719 papers from AI conferences using hierarchical latent tree analysis, enabling users to browse topics from general to fine-grained levels and detect emerging topics.
Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have been automatically categorized by a topic model. The catalog contains 7719 papers from the proceedings of two artificial intelligence conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet Allocation, we use a recently proposed method called hierarchical latent tree analysis for topic modeling. The resulting topic model contains a hierarchy of topics so that users can browse the topics from the top level to the bottom level. 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. It also can detect topics that have emerged recently.