Embedded Topic Models
Embedded Topic Models is a probabilistic topic modeling framework that integrates pre-trained word embeddings into the generative process of topics, distinguishing itself from traditional topic models by leveraging semantic information from continuous vector representations to guide topic inference.
43
Publications
1.6K
Citations
148
Authors
66
Institutions
Publications per year
2017–2026
31
Variants
Etm
Parents
43
Probabilistic latent semantic visualization
Tomoharu Iwata, Takeshi Yamada, Naonori Ueda · 2008 · 84 citations
Topic2Vec: Learning distributed representations of topics
Liqiang Niu, Xinyu Dai, Jianbing Zhang et al. · 2015 · 63 citations
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McGill University
Montreal, Canada
4 h-index
20 publications
564 citations
Tencent (China)
Shenzhen, China
3 h-index
3 publications
138 citations
National Center for Advancing Translational Sciences
Rockville, United States
2 h-index
6 publications
132 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
5 publications
271 citations
University of Southern California
Los Angeles, United States
2 h-index
3 publications
85 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
4 publications
210 citations

Beijing Institute of Technology
Beijing, China
2 h-index
7 publications
190 citations
Tsinghua University
Beijing, China
2 h-index
2 publications
47 citations
1–8 of 66
Repository · 5 publications · 170 citations
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2 publications · 44 citations
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TKDE · 2 publications · 71 citations
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1 publications · 25 citations
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POMS · 1 publications · 19 citations
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