Latent Semantic Analysis
Latent Semantic Analysis is a dimensionality reduction technique in natural language processing that uses singular value decomposition to identify the underlying semantic structure of a text corpus by mapping terms and documents into a reduced vector space where co-occurring patterns are captured through latent dimensions.
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2017–2026
467
1.7K
Indexing by latent semantic analysis
Scott Deerwester, Susan Dumais, George W. Furnas et al. · Journal of the American Society for Information Science · 1990 · 12.8K citations
An introduction to latent semantic analysis
Thomas K. Landauer, Peter W. Foltz, Darrell Laham · Discourse Processes · 1998 · 4.9K citations
Software Framework for Topic Modelling with Large Corpora
Petr Sojka · 2010 · 3.8K citations · Full text
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Thomas Hofmann · Machine Learning · 2001 · 2.5K citations · Full text
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University of Colorado Boulder
Boulder, United States
26 h-index
55 publications
14.8K citations
Carnegie Mellon University
Pittsburgh, United States
23 h-index
70 publications
5.6K citations

Chinese Academy of Sciences
Beijing, China
21 h-index
99 publications
7.3K citations
University of Memphis
Memphis, United States
20 h-index
72 publications
3.1K citations
Microsoft Research Asia (China)
Beijing, China
18 h-index
47 publications
3.5K citations
Microsoft (United States)
Redmond, United States
17 h-index
48 publications
16.3K citations

Peking University
Beijing, China
16 h-index
51 publications
3.6K citations
University of Illinois Urbana-Champaign
Urbana, United States
16 h-index
26 publications
5.7K citations
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Repository · 32 publications · 3.7K citations
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Conference · 21 publications · 1.8K citations
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13 publications · 890 citations
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TKDE · 14 publications · 2K citations
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13 publications · 1K citations
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