Temporality Detection
Temporality Detection is a computational task in natural language processing for phenotyping that identifies and classifies the temporal characteristics of clinical events within text, distinguishing it from other information extraction tasks by focusing specifically on the time-related status, such as whether an event is current, past, future, or hypothetical, in relation to the patient's timeline.
33
Publications
1.6K
Citations
112
Authors
58
Institutions
Publications per year
2017–2026
9
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SemEval-2017 Task 12: Clinical TempEval
Steven Bethard, Guergana Savova, Martha Palmer et al. · 2017 · 124 citations · Full text
Jiwei Li, Claire Cardie · 2014 · 84 citations
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Stanford University
Stanford, United States
3 h-index
3 publications
58 citations
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Orsay, France
3 h-index
9 publications
499 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
11 publications
553 citations
Université Paris-Sud
Orsay, France
3 h-index
10 publications
526 citations
Université Paris-Saclay
Gif-sur-Yvette, France
2 h-index
8 publications
472 citations
Laboratoire d'Intégration des Systèmes et des Technologies
Palaiseau, France
2 h-index
8 publications
472 citations
King's College London
London, United Kingdom
2 h-index
15 publications
507 citations
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Paris, France
2 h-index
8 publications
472 citations
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4 publications · 502 citations
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JAMIA · 3 publications · 144 citations
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1 publications · 24 citations
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1 publications · 27 citations
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Repository · 1 publications · 10 citations
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