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
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Neural Networks For Negation Scope Detection
Federico Fancellu, Adam Lopez, Bonnie Webber · 2016 · 96 citations · Full text
Speculation and Negation Scope Detection via Convolutional Neural Networks
Qian Zhong, Peifeng Li, Qiaoming Zhu et al. · 2016 · 83 citations · Full text
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Hualiang Wang, Yi Li, Huifeng Yao et al. · 2023 · 78 citations
1–5 of 29
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Hospital Universitario Puerta de Hierro Majadahonda, Spain
2 publications · 57 citations
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1–5 of 100
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University of Edinburgh
Edinburgh, United Kingdom
3 h-index
6 publications
340 citations

Universidad Politécnica de Madrid
Madrid, Spain
2 h-index
6 publications
171 citations

Boston Children's Hospital
Boston, United States
2 h-index
5 publications
366 citations

Guangdong University of Foreign Studies
Guangzhou, China
2 h-index
3 publications
60 citations
Mayo Clinic in Florida
Jacksonville, United States
2 h-index
5 publications
664 citations
Hospital Universitario Puerta de Hierro Majadahonda
Madrid, Spain
2 h-index
4 publications
114 citations
Harvard University
Cambridge, United States
2 h-index
5 publications
366 citations
Wuhan University
Wuhan, China
2 h-index
3 publications
66 citations
1–8 of 51
Repository · 2 publications · 67 citations
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Repository · 2 publications · 38 citations
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1 publications · 19 citations
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1 publications · 122 citations
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Conference · 1 publications · 17 citations
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