Automatic extraction of layman names for ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Automatic extraction of layman names for technical medical terms
Auteur(s) :
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Hamon, Thierry [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Université Paris 13 [UP13]
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Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Hamon, Thierry [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Université Paris 13 [UP13]
Titre de la manifestation scientifique :
IEEE International Conference on Healthcare Informatics
Ville :
Verona
Pays :
Italie
Date de début de la manifestation scientifique :
2014-01-01
Mot(s)-clé(s) en anglais :
Natural Language Processsing
Readability
Health Literacy
Paraphrase
Readability
Health Literacy
Paraphrase
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Informatique et langage [cs.CL]
Résumé en anglais : [en]
Medical and health information is widespread in the modern society in light of pressing health concerns and of maintaining of healthy lifestyles. It is also available through modern media (scientific research, medical ...
Lire la suite >Medical and health information is widespread in the modern society in light of pressing health concerns and of maintaining of healthy lifestyles. It is also available through modern media (scientific research, medical blogs, clinical documents, TV and radio broadcast, novels, etc.) However, medical area conveys very specific and often opaque notions (e.g., myocardial infarction, cholecystectomy, abdominal strangulated hernia, galactose urine), which are difficult to understand by people without medical training. We propose an automatic method for the acquisition of paraphrases for technical medical terms. The paraphrases should be easier to understand than the original terms. The method is based on the morphological analysis of terms and on text mining of social media texts. Analysis of the results and their evaluation indicate that such paraphrases can indeed be found in non specialized documents and show easier understanding level. Depending on the semantics of the terms, the precision values of the extractions ranges between 6 and 100%. This kind of resources is useful for several Natural Language Processing applications (i.e., information retrieval and extraction, text simplification and health literacy, question and answering).Lire moins >
Lire la suite >Medical and health information is widespread in the modern society in light of pressing health concerns and of maintaining of healthy lifestyles. It is also available through modern media (scientific research, medical blogs, clinical documents, TV and radio broadcast, novels, etc.) However, medical area conveys very specific and often opaque notions (e.g., myocardial infarction, cholecystectomy, abdominal strangulated hernia, galactose urine), which are difficult to understand by people without medical training. We propose an automatic method for the acquisition of paraphrases for technical medical terms. The paraphrases should be easier to understand than the original terms. The method is based on the morphological analysis of terms and on text mining of social media texts. Analysis of the results and their evaluation indicate that such paraphrases can indeed be found in non specialized documents and show easier understanding level. Depending on the semantics of the terms, the precision values of the extractions ranges between 6 and 100%. This kind of resources is useful for several Natural Language Processing applications (i.e., information retrieval and extraction, text simplification and health literacy, question and answering).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :