Probabilistic Document Representation
Probabilistic Document Representation is a document-level modeling approach within natural language processing that represents documents as probability distributions over latent topics or linguistic units, distinguishing it from deterministic or discrete representations by explicitly encoding uncertainty and term co-occurrence patterns.
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8.1K
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263
Authors
130
Institutions
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2017–2026
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Unsupervised Learning by Probabilistic Latent Semantic Analysis
Thomas Hofmann · Machine Learning · 2001 · 2.5K citations · Full text
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Chinese Academy of Sciences
Beijing, China
4 h-index
13 publications
274 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
11 publications
1.2K citations
Laboratoire Traitement et Communication de l’Information
Palaiseau, France
4 h-index
5 publications
160 citations
Hebrew University of Jerusalem
Jerusalem, Israel
4 h-index
9 publications
464 citations

Télécom Paris
Palaiseau, France
4 h-index
6 publications
172 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
5 publications
160 citations
Institut Mines-Télécom
Palaiseau, France
4 h-index
5 publications
160 citations
University of California, Berkeley
Berkeley, United States
3 h-index
3 publications
519 citations
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TPAMI · 3 publications · 194 citations
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3 publications · 288 citations
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2 publications · 102 citations
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PVLDB · 2 publications · 48 citations
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2 publications · 2.7K citations
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