Hidden Topic Models

Hidden Topic Models is a type of latent variable model that discovers underlying thematic structures in text collections by probabilistically associating observed words with unobserved topics, distinguishing itself through the explicit modeling of semantic coherence and document-level topic distributions.

451

Publications

27.9K

Citations

1.3K

Authors

502

Institutions

Publications per year

2017–2026

210

Authors

1.3K

  1. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    8 publications · 964 citations

    8

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    6 publications · 636 citations

    6

    h-index

  3. JB
    Jordan Boyd‐Graber

    University of Maryland, College Park, United States

    6 publications · 555 citations

    6

    h-index

  4. KV
    Konstantin Vorontsov

    Moscow Institute of Physics and Technology, Russia

    4 publications · 210 citations

    4

    h-index

  5. XP
    Xuan-Hieu Phan

    Japan Advanced Institute of Science and Technology, Japan

    4 publications · 915 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 761 citations

    15

    h-index

  2. Expert Systems with Applications

    14 publications · 1.1K citations

    14

    h-index

  3. Knowledge-Based Systems

    11 publications · 693 citations

    11

    h-index

  4. World Wide Web

    9 publications · 206 citations

    9

    h-index

  5. Information Processing & Management

    8 publications · 668 citations

    8

    h-index

1–5