Dynamic Topic Models
Dynamic Topic Models is a probabilistic topic modeling framework that extends static topic models by incorporating temporal dynamics to capture the evolution of topics over time in a document corpus.
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20.6K
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872
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348
Institutions
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2017–2026
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David M. Blei, John Lafferty · 2006 · 2.4K citations
Qiaozhu Mei, Xu Ling, Matthew Wondra et al. · 2007 · 814 citations
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348

University of Amsterdam
Amsterdam, The Netherlands
11 h-index
21 publications
902 citations
Tsinghua University
Beijing, China
11 h-index
28 publications
1.6K citations

Zhejiang University
Hangzhou, China
7 h-index
20 publications
473 citations
Wuhan University
Wuhan, China
7 h-index
24 publications
1.4K citations
University College London
London, United Kingdom
6 h-index
12 publications
529 citations

Beihang University
Beijing, China
6 h-index
18 publications
350 citations
Stanford University
Stanford, United States
5 h-index
8 publications
12.5K citations
Yonsei University
Seoul, South Korea
5 h-index
10 publications
495 citations
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Repository · 11 publications · 667 citations
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7 publications · 257 citations
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7 publications · 270 citations
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