  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.
