Abstract

One important task of topic modeling for text anal ysis is interpretability. By discovering structure topics one is able to yield improved interpretabil ity as well as modeling accuracy. In this paper, we propose a novel topic model with a deep structure that explores both inter-topic and intra-topi structures informed by word embeddings. Specifically, our model discovers inter topic structures the form of topic hierarchies and discovers intra topic structures in the form of sub-topics, each o which is informed by word embeddings and captures a fine-grained thematic aspect of a normal topic. Extensive experiments demonstrate that our model achieves the state-of-the-art performance in terms of perplexity, document classification, and topic quality. Moreover, with topic hierarchies and sub-topics, the topics discovered in our model are more interpretable, providing an illuminating means to understand text data.