Joint Entity Extraction

Joint Entity Extraction is a method in information extraction that simultaneously identifies named entities and their semantic relationships within text, distinguishing itself from pipelined approaches by modeling entity and relation prediction as interdependent tasks within a unified framework.

227

Publications

17.6K

Citations

818

Authors

267

Institutions

Publications per year

2017–2026

184

Variants

Joint Entity And Relation Extraction

Authors

818

  1. HJ
    Heng Ji

    Rensselaer Polytechnic Institute, United States

    7 publications · 1.5K citations

    7

    h-index

  2. DJ
    Donghong Ji

    Wuhan University, China

    5 publications · 203 citations

    5

    h-index

  3. AM
    Andrew McCallum

    University of Massachusetts Amherst, United States

    5 publications · 324 citations

    5

    h-index

  4. CD
    Chris Develder

    Ghent University, Belgium

    4 publications · 688 citations

    4

    h-index

  5. CC
    Claire Cardie

    Cornell University, United States

    4 publications · 842 citations

    4

    h-index

1–5 of 818

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 750 citations

    16

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 627 citations

    10

    h-index

  3. Knowledge-Based Systems

    7 publications · 301 citations

    7

    h-index

  4. Neurocomputing

    6 publications · 433 citations

    6

    h-index

  5. IEEE Access

    6 publications · 172 citations

    6

    h-index

1–5