Relation Extraction Evaluation

Relation Extraction Evaluation is a scholarly assessment framework that systematically measures the accuracy, reliability, and effectiveness of relation extraction models or systems in identifying semantic relationships between entities within unstructured text, distinguishing it from general natural language processing evaluation by its specific focus on relational data extraction performance.

236

Publications

15.1K

Citations

683

Authors

247

Institutions

Publications per year

2017–2026

101

Authors

683

  1. RG
    Ralph Grishman

    New York University, United States

    5 publications · 523 citations

    5

    h-index

  2. AL
    Alberto Lavelli

    Fondazione Bruno Kessler, Italy

    5 publications · 201 citations

    5

    h-index

  3. MI
    Mitsuru Ishizuka

    The University of Tokyo, Japan

    5 publications · 197 citations

    5

    h-index

  4. YM
    Yutaka Matsuo

    The University of Tokyo, Japan

    5 publications · 197 citations

    5

    h-index

  5. DK
    Dietrich Klakow

    Saarland University, Germany

    4 publications · 94 citations

    4

    h-index

1–5 of 683

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 414 citations

    12

    h-index

  2. Journal of the American Medical Informatics Association

    JAMIA · 5 publications · 293 citations

    5

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 112 citations

    3

    h-index

  4. Information Processing & Management

    3 publications · 66 citations

    3

    h-index

  5. BMC Bioinformatics

    3 publications · 89 citations

    3

    h-index

1–5