Biterm Topic Modeling

Biterm Topic Modeling is a topic modeling framework that belongs to the class of generative probabilistic models for uncovering latent topics in short texts, distinguished by its use of biterms—unordered word pairs co-occurring within a short text window—as the fundamental modeling units instead of individual documents or words.

39

Publications

3K

Citations

143

Authors

61

Institutions

Publications per year

2017–2026

28

Authors

143

  1. TK
    Tim K. Mackey

    University of California San Diego, United States

    6 publications · 290 citations

    6

    h-index

  2. JL
    Jiawei Li

    University of California San Diego, United States

    5 publications · 258 citations

    5

    h-index

  3. YL
    Yanyan Lan

    Chinese Academy of Sciences, China

    4 publications · 1.8K citations

    4

    h-index

  4. NS
    Neal Shah

    Global Policy Institute, United States

    4 publications · 220 citations

    4

    h-index

  5. JG
    Jiafeng Guo

    Chinese Academy of Sciences, China

    3 publications · 1.7K citations

    3

    h-index

1–5 of 143

Venues

  1. IEEE Access

    2 publications · 55 citations

    2

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 89 citations

    2

    h-index

  3. 1

    h-index

  4. Procedia Computer Science

    1 publications · 76 citations

    1

    h-index

  5. JMIR Public Health and Surveillance

    1 publications · 155 citations

    1

    h-index

1–5