Joint Relation Extraction

Joint Relation Extraction is a method in information extraction that simultaneously identifies entities and their relational predicates within text, distinguishing itself from pipeline approaches by modeling entity recognition and relation classification as interdependent tasks within a unified framework.

194

Publications

15.6K

Citations

646

Authors

191

Institutions

Publications per year

2017–2026

157

Authors

646

  1. HJ
    Heng Ji

    University of Illinois Urbana-Champaign, United States

    5 publications · 279 citations

    5

    h-index

  2. TM
    Tom M. Mitchell

    Carnegie Mellon University, United States

    5 publications · 769 citations

    5

    h-index

  3. HJ
    Heng Ji

    Rensselaer Polytechnic Institute, United States

    5 publications · 1K citations

    5

    h-index

  4. HH
    Heyan Huang

    Beijing Institute of Technology, China

    4 publications · 249 citations

    4

    h-index

  5. CD
    Chris Develder

    Ghent University, Belgium

    4 publications · 688 citations

    4

    h-index

1–5 of 646

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 646 citations

    14

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 823 citations

    11

    h-index

  3. Knowledge-Based Systems

    6 publications · 260 citations

    6

    h-index

  4. Expert Systems with Applications

    5 publications · 528 citations

    6

    h-index

  5. Neurocomputing

    4 publications · 392 citations

    5

    h-index

1–5