Distantly Supervised Sequence Labeling

Distantly Supervised Sequence Labeling is a sequence labeling approach that leverages external, noisy sources of supervision—such as heuristic rules or knowledge bases—to automatically generate training labels without manual annotation, thereby reducing reliance on costly human-labeled data while addressing the misalignment between indirect supervision and true token-level labels.

7

Publications

341

Citations

37

Authors

5

Institutions

Publications per year

2017–2026

7

Authors

37

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    3 publications · 285 citations

    3

    h-index

  2. XW
    Xuan Wang

    University of Illinois Urbana-Champaign, United States

    2 publications · 52 citations

    2

    h-index

  3. TR
    Teng Ren

    Shanghai Jiao Tong University, China

    1 publications · 233 citations

    1

    h-index

  4. SG
    Shweta Garg

    University of Illinois Urbana-Champaign, United States

    1 publications · 23 citations

    1

    h-index

  5. SZ
    Sheng Zhang

    National University of Defense Technology, China

    1 publications · 22 citations

    1

    h-index

1–5 of 37

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 33 citations

    2

    h-index

  2. 1

    h-index

  3. Research Repository UCD (University College Dublin)

    Repository · 1 publications · 10 citations

    1

    h-index

  4. Applied Intelligence

    1 publications · 13 citations

    1

    h-index

1–4