Mixture Models For Text

Mixture Models for Text is a probabilistic modeling framework that represents text collections as a combination of multiple latent topic distributions, where each document is generated from a mixture of underlying generative components specific to textual data.

206

Publications

22.6K

Citations

506

Authors

204

Institutions

Publications per year

2017–2026

33

Authors

506

  1. MO
    Mari Ostendorf

    University of Washington, United States

    6 publications · 492 citations

    6

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    5 publications · 2.7K citations

    5

    h-index

  3. AB
    Arindam Banerjee

    University of Minnesota, United States

    5 publications · 400 citations

    5

    h-index

  4. CZ
    ChengXiang Zhai

    University of Illinois Urbana-Champaign, United States

    4 publications · 1.2K citations

    4

    h-index

  5. DH
    Daniel Hsu

    Columbia University, United States

    4 publications · 426 citations

    4

    h-index

1–5 of 506

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 2.9K citations

    11

    h-index

  2. Information Processing & Management

    4 publications · 270 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 199 citations

    4

    h-index

  4. Neural Information Processing Systems

    Conference · 3 publications · 971 citations

    3

    h-index

  5. Natural Language Engineering

    3 publications · 479 citations

    3

    h-index

1–5