Mixture Models For Text
Mixture Models for Text is a probabilistic modeling framework that represents text collections as a combination of multiple latent topic distributions, where each document is generated from a mixture of underlying generative components specific to textual data.
206
Publications
22.6K
Citations
506
Authors
204
Institutions
Publications per year
2017–2026
33
Parents
206
Probabilistic Latent Semantic Analysis
Thomas Hofmann · arXiv (Cornell University) · 2013 · 2.1K citations · Full text
The author-topic model for authors and documents
Michal Rosen‐Zvi, Thomas Griffiths, Mark Steyvers et al. · Uncertainty in Artificial Intelligence · 2004 · 1.3K citations
Xuerui Wang, Andrew McCallum · 2006 · 1.1K citations
A correlated topic model of Science
The Annals of Applied Statistics · 2007 · 944 citations · Full text
1–5 of 206
506
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University of Illinois Urbana-Champaign, United States
4 publications · 1.2K citations
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4
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1–5 of 506
204
University of Illinois Urbana-Champaign
Urbana, United States
9 h-index
26 publications
6K citations

University of Massachusetts Amherst
Amherst Center, United States
8 h-index
17 publications
3.9K citations
Princeton University
Princeton, United States
6 h-index
8 publications
4.3K citations
Yahoo (United States)
Sunnyvale, United States
6 h-index
16 publications
681 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
7 publications
470 citations
Tsinghua University
Beijing, China
5 h-index
10 publications
1.7K citations
Stanford University
Stanford, United States
5 h-index
8 publications
2.1K citations
Microsoft (United States)
Redmond, United States
5 h-index
9 publications
327 citations
1–8 of 204
Repository · 12 publications · 2.9K citations
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4 publications · 270 citations
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Conference · 4 publications · 199 citations
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Conference · 3 publications · 971 citations
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3 publications · 479 citations
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