Semi-supervised Topic Modeling
Semi-Supervised Topic Modeling is a topic modeling approach that incorporates partial human guidance or labeled data during the discovery of latent topics to improve interpretability or accuracy, distinguishing it from fully unsupervised and supervised variants by leveraging both labeled and unlabeled documents.
63
Publications
3.6K
Citations
220
Authors
96
Institutions
Publications per year
2017–2026
25
Parents
63
1–5 of 63
220
3
h-index
2
h-index
Korea Advanced Institute of Science and Technology, South Korea
2 publications · 353 citations
2
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2
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2
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1–5 of 220
96

Harvard University Press
Cambridge, United States
3 h-index
7 publications
157 citations
National University of Singapore
Singapore, Singapore
3 h-index
9 publications
440 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
249 citations

Indian Institute of Technology Madras
Chennai, India
2 h-index
4 publications
319 citations

Southeast University
Nanjing, China
2 h-index
3 publications
82 citations
University of Maryland, College Park
College Park, United States
2 h-index
3 publications
173 citations

Beijing University of Technology
Beijing, China
2 h-index
2 publications
34 citations

Zhejiang University
Hangzhou, China
2 h-index
10 publications
105 citations
1–8 of 96
2 publications · 73 citations
2
h-index
TVCG · 2 publications · 353 citations
2
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2 publications · 120 citations
2
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2 publications · 73 citations
2
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1 publications · 68 citations
1
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1–5