Joint Entity Recognition

Joint Entity Recognition is a methodological framework in natural language processing that simultaneously identifies and classifies multiple types of named entities within a text, distinguishing itself from pipeline approaches by modeling entity boundaries and types in a unified, interdependent manner.

148

Publications

11.4K

Citations

540

Authors

194

Institutions

Publications per year

2017–2026

116

Authors

540

  1. JD
    Johannes Deleu

    Ghent University, Belgium

    4 publications · 688 citations

    4

    h-index

  2. CD
    Chris Develder

    Ghent University, Belgium

    4 publications · 688 citations

    4

    h-index

  3. TD
    Thomas Demeester

    Ghent University, Belgium

    4 publications · 688 citations

    4

    h-index

  4. RH
    Ruizhang Huang

    Guizhou University, China

    3 publications · 50 citations

    3

    h-index

  5. DJ
    Donghong Ji

    Wuhan University, China

    3 publications · 136 citations

    3

    h-index

1–5 of 540

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 513 citations

    12

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 539 citations

    8

    h-index

  3. Knowledge-Based Systems

    7 publications · 289 citations

    7

    h-index

  4. Neurocomputing

    4 publications · 395 citations

    4

    h-index

  5. IEEE Access

    4 publications · 58 citations

    4

    h-index

1–5