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.
@cite_1: Effectively exploring and analyzing large text corpora requires visualizations that provide a high level summary. Past work has relied on faceted browsing of document metadata or on natural language processing of document text. In this paper, we present a new web-based tool that integrates topics learned from an unsupervised topic model in a faceted browsing experience. The user can manage topics, filter documents by topic and summarize views with metadata and topic graphs. We report a user study of the usefulness of topics in our tool.
@cite_12: We present LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3. Our visualization provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model.
@cite_6: 
@cite_11: Existing algorithms for understanding large collections of documents often produce output that is nearly as difficult and time consuming to interpret as reading each of the documents themselves. Topic modeling is a text understanding algorithm that discovers the “topics” or themes within a collection of documents. Tools based on topic modeling become increasingly complex as the number of topics required to best represent the collection increases. In this work, we present Hi ´ erarchie, an interactive visualization that adds structure to large topic models, making them approachable and useful to an end user. Additionally, we demonstrate Hi ´ erarchie’s ability to analyze a diverse document set regarding a trending news topic.
Topic browsing tools have been built based on topic models. Many tools use LDA for topic modeling @cite_11 @cite_6 @cite_1 @cite_12 . They do not show any hierarchy of topics. attempts to build a tool with a topic hierarchy by recursively splitting and remodeling a corpus based on LDA. Unlike HLTA, the topic model does not have a strong statistical basis.