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
Parents
61
A metalearning approach to processing the scope of negation
Roser Morante, Walter Daelemans · 2009 · 153 citations · Full text
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Yifan Peng, Xiaosong Wang, Le Lü et al. · PubMed · 2017 · 130 citations · Full text
Learning the scope of hedge cues in biomedical texts
Roser Morante, Walter Daelemans · 2009 · 120 citations · Full text
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University of Edinburgh
Edinburgh, United Kingdom
5 h-index
12 publications
513 citations

University of Freiburg
Freiburg im Breisgau, Germany
4 h-index
7 publications
233 citations

Peking University
Beijing, China
3 h-index
3 publications
66 citations

Universidad de Huelva
Huelva, Spain
3 h-index
6 publications
364 citations
University of Antwerp
Antwerp, Belgium
3 h-index
7 publications
870 citations

Stockholm University
Stockholm, Sweden
2 h-index
8 publications
126 citations
National Institutes of Health
Bethesda, United States
2 h-index
4 publications
484 citations

Harbin Institute of Technology
Harbin, China
2 h-index
9 publications
154 citations
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Repository · 6 publications · 273 citations
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Repository · 3 publications · 179 citations
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3 publications · 88 citations
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2 publications · 73 citations
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2 publications · 28 citations
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