Understanding of unknown medical words
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Understanding of unknown medical words
Auteur(s) :
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]

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]
Titre de la manifestation scientifique :
Biomedical NLP Workshop associated with RANLP 2017
Ville :
Varna
Pays :
Bulgarie
Date de début de la manifestation scientifique :
2017-09-08
Date de publication :
2017-09-08
Mot(s)-clé(s) en anglais :
Natural Language Processing
Literacy
Medical terms
Literacy
Medical terms
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Informatique et langage [cs.CL]
Résumé en anglais : [en]
We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, ...
Lire la suite >We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and anal-ysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some ofthem distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.Lire moins >
Lire la suite >We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and anal-ysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some ofthem distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :