Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding-Reference-Cited by-同舟云学术

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

Published:2020-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Meng Yu¹,Zhang Yunyi¹,Huang Jiaxin¹,Zhang Yu¹,Zhang Chao²,Han Jiawei¹

Affiliation:

1. University of Illinois at Urbana-Champaign, Champaign, IL, USA

2. Georgia Institute of Technology, Atlanta, GA, USA

Funder

National Science Foundation

Defense Advanced Research Projects Agency

Defense Threat Reduction Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403242

Reference37 articles.

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2. David Andrzejewski and Xiaojin Zhu. 2009. Latent Dirichlet Allocation with Topic-in-Set Knowledge. In HLT-NAACL. David Andrzejewski and Xiaojin Zhu. 2009. Latent Dirichlet Allocation with Topic-in-Set Knowledge. In HLT-NAACL.

3. Kayhan Batmanghelich Ardavan Saeedi Karthik Narasimhan and Sam Gershman. 2016. Nonparametric spherical topic modeling with word embeddings. In ACL. 537. Kayhan Batmanghelich Ardavan Saeedi Karthik Narasimhan and Sam Gershman. 2016. Nonparametric spherical topic modeling with word embeddings. In ACL. 537.

4. David M. Blei Thomas L. Griffiths Michael I. Jordan and Joshua B. Tenenbaum. 2003 a. Hierarchical Topic Models and the Nested Chinese Restaurant Process. In NIPS. David M. Blei Thomas L. Griffiths Michael I. Jordan and Joshua B. Tenenbaum. 2003 a. Hierarchical Topic Models and the Nested Chinese Restaurant Process. In NIPS.

5. David M Blei and Jon D Mcauliffe. 2008. Supervised topic models. In NIPS. 121--128. David M Blei and Jon D Mcauliffe. 2008. Supervised topic models. In NIPS. 121--128.

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