Semi-supervised Information Extraction

Semi-Supervised Information Extraction is an information extraction paradigm that leverages a small amount of labeled data in conjunction with a large volume of unlabeled data to learn extraction models, distinguishing itself from fully supervised and unsupervised approaches by balancing human-labeled examples with automated generalization over unlabeled instances.

87

Publications

4K

Citations

283

Authors

115

Institutions

Publications per year

2017–2026

29

Authors

283

  1. TM
    Tom M. Mitchell

    Carnegie Mellon University, United States

    5 publications · 685 citations

    5

    h-index

  2. JA
    J. Andrew Carlson

    Albany Medical Center Hospital, United States

    4 publications · 548 citations

    4

    h-index

  3. JB
    Justin Betteridge

    Carnegie Mellon University, United States

    3 publications · 534 citations

    3

    h-index

  4. EH
    Estevam Hruschka

    Universidade Federal de São Carlos, Brazil

    3 publications · 534 citations

    3

    h-index

  5. RG
    Ralph Grishman

    New York University, United States

    3 publications · 150 citations

    3

    h-index

1–5 of 283

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 77 citations

    5

    h-index

  2. 2

    h-index

  3. BMC Bioinformatics

    2 publications · 63 citations

    2

    h-index

  4. PubMed

    Repository · 2 publications · 40 citations

    2

    h-index

  5. Knowledge and Information Systems

    2 publications · 42 citations

    2

    h-index

1–5