Inter and intra topic structure learning with word embeddings

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publicationInternational Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden
EditorsJennifer Dy, Andreas Krause
Place of PublicationStroudsburg PA USA
PublisherProceedings of Machine Learning Research (PMLR)
Number of pages10
ISBN (Electronic)9781510867963
Publication statusPublished - 2018
EventInternational Conference on Machine Learning 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018


ConferenceInternational Conference on Machine Learning 2018

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