Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis

TL;DR

This paper introduces an online research paper catalog that uses hierarchical latent tree analysis for topic modeling, enabling effective browsing of large AI literature collections with hierarchical and emerging topics detection.

Contribution

It presents a novel application of hierarchical latent tree analysis for organizing research papers into a hierarchical topic model for improved browsing.

Findings

Catalog contains 7719 papers from AI conferences (2000-2015)

Hierarchical topic model enables browsing from general to specific topics

Detects recently emerged research topics

Abstract

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.