Neural Coreference Resolution

Neural Coreference Resolution is a machine learning approach to coreference resolution that employs neural network architectures to model and predict co-reference relations between textual expressions by learning distributed representations of linguistic context.

54

Publications

2K

Citations

174

Authors

72

Institutions

Publications per year

2017–2026

46

Authors

174

  1. QY
    Qingyu Yin

    Harbin Institute of Technology, China

    3 publications · 100 citations

    3

    h-index

  2. BV
    Benjamin Van Durme

    Johns Hopkins University, United States

    3 publications · 72 citations

    3

    h-index

  3. CD
    Christopher D. Manning

    Stanford University, United States

    3 publications · 107 citations

    3

    h-index

  4. PX
    Patrick Xia

    Johns Hopkins University, United States

    3 publications · 72 citations

    3

    h-index

  5. WZ
    Weinan Zhang

    Harbin Institute of Technology, China

    2 publications · 54 citations

    2

    h-index

1–5 of 174

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 185 citations

    6

    h-index

  2. IEEE Access

    2 publications · 36 citations

    2

    h-index

  3. Information Processing & Management

    2 publications · 56 citations

    2

    h-index

  4. ACM Transactions on Information Systems

    1 publications · 55 citations

    1

    h-index

  5. Deep Blue (University of Michigan)

    Repository · 1 publications · 26 citations

    1

    h-index

1–5