Automatic Prediction of Semantic Labels ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Automatic Prediction of Semantic Labels for French Medical Terms
Auteur(s) :
Hamon, Thierry [Auteur]
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Université Paris 13 [UP13]
Sciences et Technologies des Langues - LISN [STL]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Laboratoire Interdisciplinaire des Sciences du Numérique [LISN]
Université Paris 13 [UP13]
Sciences et Technologies des Langues - LISN [STL]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Titre de la manifestation scientifique :
Medical Informatics Europe conference (MIE2022)
Ville :
Nice
Pays :
France
Date de début de la manifestation scientifique :
2022-05-27
Titre de l’ouvrage :
Proceedings of the Medical Informatics Europe conference, MIE 202022
Titre de la revue :
Studies in Health Technology and Informatics
Éditeur :
IOS Press
Date de publication :
2022-05-25
Mot(s)-clé(s) en anglais :
Semantic labeling
NLP
Machine learning
Terminology
French
NLP
Machine learning
Terminology
French
Discipline(s) HAL :
Informatique [cs]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
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