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.
|Title of host publication||Proceedings of Machine Learning Research|
|Subtitle of host publication||International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden|
|Editors||Jennifer Dy, Andreas Krause|
|Place of Publication||Stroudsburg PA USA|
|Publisher||Proceedings of Machine Learning Research (PMLR)|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||International Conference on Machine Learning 2018 - Stockholm, Sweden|
Duration: 10 Jul 2018 → 15 Jul 2018
|Conference||International Conference on Machine Learning 2018|
|Period||10/07/18 → 15/07/18|
Zhao, H., Du, L., Buntine, W., & Zhou, M. (2018). Inter and intra topic structure learning with word embeddings. In J. Dy, & A. Krause (Eds.), Proceedings of Machine Learning Research: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden (Vol. 80, pp. 5892-5901). Stroudsburg PA USA: Proceedings of Machine Learning Research (PMLR).