Probabilistic Document Representation

Probabilistic Document Representation is a document-level modeling approach within natural language processing that represents documents as probability distributions over latent topics or linguistic units, distinguishing it from deterministic or discrete representations by explicitly encoding uncertainty and term co-occurrence patterns.

104

Publications

8.1K

Citations

263

Authors

130

Institutions

Publications per year

2017–2026

18

Authors

263

  1. YS
    Yehoshua Sagiv

    Hebrew University of Jerusalem, Israel

    4 publications · 213 citations

    4

    h-index

  2. BK
    Benny Kimelfeld

    Technion – Israel Institute of Technology, Israel

    4 publications · 213 citations

    4

    h-index

  3. PS
    Pierre Senellart

    Télécom Paris, France

    4 publications · 141 citations

    4

    h-index

  4. NF
    Norbert Fuhr

    University of Duisburg-Essen, Germany

    3 publications · 497 citations

    3

    h-index

  5. CZ
    ChengXiang Zhai

    University of Illinois Urbana-Champaign, United States

    2 publications · 276 citations

    2

    h-index

1–5 of 263

Venues

  1. 3

    h-index

  2. Information Retrieval

    3 publications · 288 citations

    3

    h-index

  3. The VLDB Journal

    2 publications · 102 citations

    2

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 48 citations

    2

    h-index

  5. Machine Learning

    2 publications · 2.7K citations

    2

    h-index

1–5