Semi-supervised Topic Modeling

Semi-Supervised Topic Modeling is a topic modeling approach that incorporates partial human guidance or labeled data during the discovery of latent topics to improve interpretability or accuracy, distinguishing it from fully unsupervised and supervised variants by leveraging both labeled and unlabeled documents.

63

Publications

3.6K

Citations

220

Authors

96

Institutions

Publications per year

2017–2026

25

Authors

220

  1. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    3 publications · 142 citations

    3

    h-index

  2. JB
    Jordan Boyd‐Graber

    University of Maryland, College Park, United States

    2 publications · 259 citations

    2

    h-index

  3. JC
    Jaegul Choo

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 353 citations

    2

    h-index

  4. TN
    Thang Nguyen

    University of Maryland, College Park, United States

    2 publications · 259 citations

    2

    h-index

  5. WZ
    Weigang Zhang

    University of Chinese Academy of Sciences, China

    2 publications · 34 citations

    2

    h-index

1–5 of 220

Venues

  1. IEEE Access

    2 publications · 73 citations

    2

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 353 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 120 citations

    2

    h-index

  4. 2

    h-index

  5. Information and Software Technology

    1 publications · 68 citations

    1

    h-index

1–5