Hierarchical Topic Models

Hierarchical Topic Models is a type of probabilistic topic model that extends flat topic structures by organizing topics into a hierarchy to capture broader and more fine-grained thematic relationships in text corpora.

344

Publications

26.5K

Citations

993

Authors

376

Institutions

Publications per year

2017–2026

123

Authors

993

  1. DM
    David M. Blei

    Princeton University, United States

    11 publications · 3.7K citations

    11

    h-index

  2. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    6 publications · 194 citations

    6

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  3. JP
    John Paisley

    Columbia University, United States

    6 publications · 2.1K citations

    6

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  4. WB
    Wray Buntine

    Monash University, Australia

    6 publications · 237 citations

    6

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  5. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    5 publications · 482 citations

    5

    h-index

1–5 of 993

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 506 citations

    10

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  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 7 publications · 549 citations

    7

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  3. 6

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  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 465 citations

    6

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  5. Neural Information Processing Systems

    Conference · 6 publications · 1.2K citations

    6

    h-index

1–5