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Inter and Intra Topic Structure Learning with Word Embeddings
He Zhao · Lan Du · Wray Buntine · Mingyuan Zhou

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #177

One important task of topic modeling for text analysis is interpretability. By discovering structured topics one is able to yield improved interpretability 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-topic structures informed by word embeddings. Specifically, our model discovers inter topic structures in the form of topic hierarchies and discovers intra topic structures in the form of sub-topics, each of 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.

Author Information

He Zhao (FIT, Monash University)
Lan Du (Faculty of Information Technology, Monash University)
Wray Buntine (Monash University)
Mingyuan Zhou (University of Texas at Austin)

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