Understanding of unknown medical words
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Understanding of unknown medical words
Author(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]
Conference title :
Biomedical NLP Workshop associated with RANLP 2017
City :
Varna
Country :
Bulgarie
Start date of the conference :
2017-09-08
Publication date :
2017-09-08
English keyword(s) :
Natural Language Processing
Literacy
Medical terms
Literacy
Medical terms
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Informatique et langage [cs.CL]
English abstract : [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, ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :