Machine Learning-based Negation Detection

Machine Learning-Based Negation Detection is a computational methodology within natural language processing that employs machine learning algorithms to automatically identify and classify negation cues and their linguistic scope in textual data, distinguishing itself from rule-based or symbolic approaches by relying on data-driven models trained on annotated corpora.

61

Publications

2.6K

Citations

203

Authors

104

Institutions

Publications per year

2017–2026

33

Authors

203

  1. BW
    Bonnie Webber

    University of Edinburgh, United Kingdom

    5 publications · 180 citations

    5

    h-index

  2. FF
    Federico Fancellu

    University of Edinburgh, United Kingdom

    5 publications · 180 citations

    5

    h-index

  3. AL
    Adam Lopez

    University of Edinburgh, United Kingdom

    4 publications · 164 citations

    4

    h-index

  4. SF
    Stefan Feuerriegel

    ETH Zurich, Switzerland

    4 publications · 111 citations

    4

    h-index

  5. DN
    Dirk Neumann

    University of Freiburg, Germany

    3 publications · 94 citations

    3

    h-index

1–5 of 203

Venues

  1. PubMed

    Repository · 6 publications · 273 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 179 citations

    3

    h-index

  3. Natural Language Engineering

    3 publications · 88 citations

    3

    h-index

  4. BMC Bioinformatics

    2 publications · 73 citations

    2

    h-index

  5. Applied Sciences

    2 publications · 28 citations

    2

    h-index

1–5