Generalizability of readability models for ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Generalizability of readability models for medical terms
Author(s) :
Pylieva, Hanna [Auteur]
Chernodub, Artem [Auteur]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Hamon, Thierry [Auteur]
Université Paris 13 [UP13]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Chernodub, Artem [Auteur]
Grabar, Natalia [Auteur]

Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Hamon, Thierry [Auteur]
Université Paris 13 [UP13]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Conference title :
International Congress on Medical Informatics
Conference organizers(s) :
L. Ohno-Machado and B. Séroussi (eds.)
City :
Lyon
Country :
France
Start date of the conference :
2019-08-01
English keyword(s) :
Natural Language Processing
Terminology
Health Information Systems
Terminology
Health Information Systems
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Informatique et langage [cs.CL]
English abstract : [en]
Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms ...
Show more >Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators. On the basis of these data, we propose cross-validation scenarios in order to test the generalization ability of models to detect the difficulty of medical words. On data provided by seven annotators, we show that the models are generalizable from one annotator to another.Show less >
Show more >Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators. On the basis of these data, we propose cross-validation scenarios in order to test the generalization ability of models to detect the difficulty of medical words. On data provided by seven annotators, we show that the models are generalizable from one annotator to another.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :