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Academic researchers often need to face with a large collection of researchpapers in the literature. This problem may be even worse for postgraduatestudents who are new to a field and may not know where to start. To addressthis problem, we have developed an online catalog of research papers where thepapers have been automatically categorized by a topic model. The catalogcontains 7719 papers from the proceedings of two artificial intelligenceconferences from 2000 to 2015. Rather than the commonly used Latent DirichletAllocation, we use a recently proposed method called hierarchical latent treeanalysis for topic modeling. The resulting topic model contains a hierarchy oftopics so that users can browse the topics from the top level to the bottomlevel. The topic model contains a manageable number of general topics at thetop level and allows thousands of fine-grained topics at the bottom level. Italso can detect topics that have emerged recently.

Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis