Dense Document Representation

Dense Document Representation is a document-level vectorial encoding in which semantic information is embedded into a continuous, low-dimensional space through neural or statistical models, distinguishing it from sparse, high-dimensional representations by capturing contextual and syntactic nuances via distributed feature activation.

39

Publications

7.6K

Citations

133

Authors

52

Institutions

Publications per year

2017–2026

31

Authors

133

  1. HZ
    Hamed Zamani

    University of Massachusetts Amherst, United States

    3 publications · 265 citations

    3

    h-index

  2. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    3 publications · 265 citations

    3

    h-index

  3. MC
    Michael Collins

    University of Calgary, Canada

    2 publications · 379 citations

    2

    h-index

  4. LG
    Luyu Gao

    Carnegie Mellon University, United States

    2 publications · 193 citations

    2

    h-index

  5. YL
    Yi Luan

    University of Washington, United States

    2 publications · 379 citations

    2

    h-index

1–5 of 133

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 5.4K citations

    6

    h-index

  2. Neurocomputing

    2 publications · 219 citations

    2

    h-index

  3. Information Processing & Management

    1 publications · 67 citations

    1

    h-index

  4. Information Sciences

    1 publications · 23 citations

    1

    h-index

  5. International Journal of Neural Systems

    1 publications · 22 citations

    1

    h-index

1–5