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

Authors

1.3K

  1. DM
    David M. Blei

    Princeton University, United States

    9 publications · 9.8K citations

    9

    h-index

  2. JB
    Jordan Boyd‐Graber

    University of Maryland, College Park, United States

    7 publications · 2.3K citations

    7

    h-index

  3. BL
    Bing Liu

    University of Illinois Chicago, United States

    5 publications · 1K citations

    5

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  4. MD
    Mark Dredze

    Johns Hopkins University, United States

    5 publications · 192 citations

    5

    h-index

  5. DG
    Daniel Gática-Pérez

    Idiap Research Institute, Switzerland

    5 publications · 482 citations

    5

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 1.8K citations

    11

    h-index

  2. Expert Systems with Applications

    6 publications · 304 citations

    6

    h-index

  3. IEEE Access

    6 publications · 140 citations

    6

    h-index

  4. 5

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  5. 4

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1–5