Deep Learning-based Negation Detection

Deep Learning-Based Negation Detection is a computational method within natural language processing that employs deep neural architectures to automatically identify and classify negation cues and their scopes in textual data, distinguishing itself from rule-based or shallow learning approaches through its reliance on hierarchical feature representations learned from large-scale annotated corpora.

29

Publications

1.1K

Citations

100

Authors

51

Institutions

Publications per year

2017–2026

24

Authors

100

  1. BW
    Bonnie Webber

    University of Edinburgh, United Kingdom

    3 publications · 130 citations

    3

    h-index

  2. FF
    Federico Fancellu

    University of Edinburgh, United Kingdom

    3 publications · 130 citations

    3

    h-index

  3. SS
    Suraj Sawant

    Savitribai Phule Pune University, India

    1 publications · 49 citations

    2

    h-index

  4. AR
    Alejandro Rodríguez‐González

    Universidad Politécnica de Madrid, Spain

    2 publications · 57 citations

    2

    h-index

  5. MP
    Mariano Provencio

    Hospital Universitario Puerta de Hierro Majadahonda, Spain

    2 publications · 57 citations

    2

    h-index

1–5 of 100

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 67 citations

    3

    h-index

  2. PubMed

    Repository · 2 publications · 38 citations

    2

    h-index

  3. BMC Bioinformatics

    1 publications · 19 citations

    1

    h-index

  4. PLoS ONE

    1 publications · 122 citations

    1

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 17 citations

    1

    h-index

1–5