Document-level Annotation

Document-Level Annotation is a type of knowledge annotation that characterizes a scholarly document as a whole by identifying and classifying its primary contribution—such as a method, model, framework, theory, construct, dataset, task, or metric—thereby distinguishing it from finer-grained annotation approaches that target individual propositions or passages within the document.

49

Publications

5.9K

Citations

230

Authors

87

Institutions

Publications per year

2017–2026

22

Authors

230

  1. KN
    Katashi Nagao

    Nagoya University, Japan

    3 publications · 124 citations

    3

    h-index

  2. WW
    Wendy W. Chapman

    University of Pittsburgh, United States

    2 publications · 406 citations

    2

    h-index

  3. RD
    Ravit Dotan

    University of Pittsburgh, United States

    2 publications · 287 citations

    2

    h-index

  4. WS
    William S. Agnew

    Yale University, United States

    2 publications · 287 citations

    2

    h-index

  5. PK
    Pratyusha Kalluri

    Stanford University, United States

    2 publications · 287 citations

    2

    h-index

1–5 of 230

Venues

  1. Journal of Biomedical Informatics

    3 publications · 471 citations

    3

    h-index

  2. JMIR Medical Informatics

    JMI · 2 publications · 48 citations

    2

    h-index

  3. Journal of the American Medical Informatics Association

    JAMIA · 2 publications · 150 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 29 citations

    2

    h-index

  5. 1

    h-index

1–5