Transformer-based Negation Detection

Transformer-Based Negation Detection is a computational linguistics method that employs transformer neural network architectures to identify and interpret negation scopes and effects in natural language text, distinguishing itself from rule-based or traditional machine learning approaches through its reliance on self-attention mechanisms and contextualized word representations.

6

Publications

263

Citations

19

Authors

11

Institutions

Publications per year

2017–2026

6

Authors

19

  1. DY
    Da Yin

    University of California, Los Angeles, United States

    2 publications · 146 citations

    2

    h-index

  2. KC
    Kai-Wei Chang

    University of California, Los Angeles, United States

    2 publications · 146 citations

    2

    h-index

  3. TM
    Tao Meng

    Central South University of Forestry and Technology, China

    2 publications · 146 citations

    2

    h-index

  4. OM
    Orlando Montenegro

    Universidad del Valle, Colombia

    1 publications · 35 citations

    1

    h-index

  5. AR
    Alejandro Rodríguez‐González

    Universidad Politécnica de Madrid, Spain

    1 publications · 35 citations

    1

    h-index

1–5 of 19

Venues

  1. Journal of the American Medical Informatics Association

    JAMIA · 1 publications · 61 citations

    1

    h-index

  2. PeerJ Computer Science

    1 publications · 35 citations

    1

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 19 citations

    1

    h-index

  4. Journal of Healthcare Informatics Research

    1 publications · 10 citations

  5. Journal of Advances in Information Technology

    JAIT · 1 publications · 10 citations

1–5