Salience Modeling

Salience Modeling is a computational framework within coreference resolution that identifies and ranks discourse entities based on their prominence in context, distinguishing itself by integrating linguistic, positional, and attentional cues to determine which entities are most likely to be referred to in subsequent expressions.

104

Publications

5.1K

Citations

260

Authors

113

Institutions

Publications per year

2017–2026

30

Authors

260

  1. MC
    Muhao Chen

    University of Southern California, United States

    3 publications · 51 citations

    3

    h-index

  2. MS
    Michael Strube

    Washington University in St. Louis, United States

    3 publications · 105 citations

    3

    h-index

  3. NA
    Noah A. Smith

    Carnegie Mellon University, United States

    2 publications · 126 citations

    2

    h-index

  4. SQ
    Shaolin Qu

    Michigan State University, United States

    2 publications · 44 citations

    2

    h-index

  5. FW
    Fei Wang

    Cornell University, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 260

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 258 citations

    9

    h-index

  2. Political Research Quarterly

    PRQ · 4 publications · 184 citations

    4

    h-index

  3. SSRN Electronic Journal

    Repository · 3 publications · 69 citations

    3

    h-index

  4. Journal of Vision

    3 publications · 123 citations

    3

    h-index

  5. Scholarly Commons (University of Pennsylvania)

    Repository · 2 publications · 21 citations

    2

    h-index

1–5