Unsupervised Topic Modeling
Unsupervised Topic Modeling is a computational method in natural language processing that automatically discovers latent thematic structures in a corpus without relying on labeled data, distinguishing itself from supervised approaches by inferring topics solely from the statistical patterns of word co-occurrence.
427
Publications
30.9K
Citations
1.3K
Authors
524
Institutions
Publications per year
2017–2026
202
Parents
427
David M. Blei · Communications of the ACM · 2012 · 5.5K citations
Reading Tea Leaves: How Humans Interpret Topic Models
Jonathan Chang, Sean Gerrish, Chong Wang et al. · 2009 · 1.9K citations
David M. Blei, Jon McAuliffe · arXiv (Cornell University) · 2010 · 1.3K citations · Full text
Discovering objects and their location in images
Josef Šivic, Bryan Russell, Alexei A. Efros et al. · 2005 · 984 citations · Full text
1–5 of 427
1.3K
9
h-index
University of Maryland, College Park, United States
7 publications · 2.3K citations
7
h-index
5
h-index
5
h-index
5
h-index
1–5 of 1.3K
524
Carnegie Mellon University
Pittsburgh, United States
14 h-index
33 publications
2.3K citations
Stanford University
Stanford, United States
12 h-index
21 publications
3K citations
University of California, Irvine
Irvine, United States
10 h-index
27 publications
4.3K citations
Massachusetts Institute of Technology
Cambridge, United States
9 h-index
16 publications
2.9K citations
University of Maryland, College Park
College Park, United States
8 h-index
16 publications
3.6K citations
University of Cambridge
Cambridge, United Kingdom
7 h-index
11 publications
580 citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
21 publications
580 citations
Microsoft (United States)
Redmond, United States
7 h-index
10 publications
934 citations
1–8 of 524
Repository · 12 publications · 1.8K citations
11
h-index
6 publications · 304 citations
6
h-index
6 publications · 140 citations
6
h-index
TACL · 5 publications · 118 citations
5
h-index
4 publications · 383 citations
4
h-index
1–5