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
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Parents
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1–5 of 6
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University of California, Los Angeles
Los Angeles, United States
2 h-index
4 publications
400 citations

Peking University
Beijing, China
2 h-index
4 publications
400 citations
King University
Bristol, United States
1 h-index
3 publications
381 citations

Universidad Politécnica de Madrid
Madrid, Spain
1 h-index
3 publications
105 citations

Universidad del Valle
Santiago de Cali, Colombia
1 h-index
2 publications
70 citations
Moscow Institute of Thermal Technology
Moscow, Russia
1 h-index
4 publications
44 citations
Harvard University
Cambridge, United States
1 h-index
4 publications
244 citations

Boston Children's Hospital
Boston, United States
1 h-index
4 publications
244 citations
1–8 of 11
JAMIA · 1 publications · 61 citations
1
h-index
1 publications · 35 citations
1
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Repository · 1 publications · 19 citations
1
h-index
1 publications · 10 citations
JAIT · 1 publications · 10 citations
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