Automatic Prediction of Semantic Labels ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Automatic Prediction of Semantic Labels for French Medical Terms
Author(s) :
Hamon, Thierry [Auteur]
Sciences et Technologies des Langues - LISN [STL]
Université Paris 13 [UP13]
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Sciences et Technologies des Langues - LISN [STL]
Université Paris 13 [UP13]
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Conference title :
Medical Informatics Europe conference (MIE2022)
City :
Nice
Country :
France
Start date of the conference :
2022-05-27
Book title :
Proceedings of the Medical Informatics Europe conference, MIE 202022
Journal title :
Studies in Health Technology and Informatics
Publisher :
IOS Press
Publication date :
2022-05-25
English keyword(s) :
Semantic labeling
NLP
Machine learning
Terminology
French
NLP
Machine learning
Terminology
French
HAL domain(s) :
Informatique [cs]
English abstract : [en]
We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic ...
Show more >We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.Show less >
Show more >We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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