Embedded Topic Models

Embedded Topic Models is a probabilistic topic modeling framework that integrates pre-trained word embeddings into the generative process of topics, distinguishing itself from traditional topic models by leveraging semantic information from continuous vector representations to guide topic inference.

43

Publications

1.6K

Citations

148

Authors

66

Institutions

Publications per year

2017–2026

31

Variants

Etm

Authors

148

  1. YL
    Yue Li

    McGill University, Canada

    4 publications · 154 citations

    4

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    3 publications · 268 citations

    3

    h-index

  3. XD
    Xinyu Dai

    Nanjing University, China

    2 publications · 92 citations

    2

    h-index

  4. LN
    Liqiang Niu

    Nanjing University of Science and Technology, China

    2 publications · 92 citations

    2

    h-index

  5. AG
    Amulya Gupta

    Iowa State University, United States

    2 publications · 30 citations

    2

    h-index

1–5 of 148

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 170 citations

    4

    h-index

  2. Scientometrics

    2 publications · 44 citations

    2

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 71 citations

    2

    h-index

  4. Cluster Computing

    1 publications · 25 citations

    1

    h-index

  5. Production and Operations Management

    POMS · 1 publications · 19 citations

    1

    h-index

1–5