Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

Banner header
69.361 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Ciències de la Computació
  • Articles de revista
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Ciències de la Computació
  • Articles de revista
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Arabic medical entity tagging using distant learning in a multilingual framework

Thumbnail
View/Open
1-s2.0-S1319157816300854-main.pdf (909,1Kb)
 
10.1016/j.jksuci.2016.10.004
 
  View UPCommons Usage Statistics
  LA Referencia / Recolecta stats
Includes usage data since 2022
Cita com:
hdl:2117/110166

Show full item record
Cotik, Viviana
Rodríguez Hontoria, HoracioMés informacióMés informacióMés informació
Vivaldi, Jorge
Document typeArticle
Defense date2017-04-30
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
A semantic tagger aiming to detect relevant entities in Arabic medical documents and tagging them with their appropriate semantic class is presented. The system takes profit of a Multilingual Framework covering four languages (Arabic, English, French, and Spanish), in a way that resources available for each language can be used to improve the results of the others, this is specially important for less resourced languages as Arabic. The approach has been evaluated against Wikipedia pages of the four languages belonging to the medical domain. The core of the system is the definition of a base tagset consisting of the three most represented classes in SNOMED-CT taxonomy and the learning of a binary classifier for each semantic category in the tagset and each language, using a distant learning approach over three widely used knowledge resources, namely Wikipedia, Dbpedia, and SNOMED-CT.
CitationCotik, V., Rodríguez, H., Vivaldi, J. Arabic medical entity tagging using distant learning in a multilingual framework. "Journal of King Saud University-Computer and Information Sciences", 30 Abril 2017, vol. 29, núm. 2, p. 204-211. 
URIhttp://hdl.handle.net/2117/110166
DOI10.1016/j.jksuci.2016.10.004
ISSN1319-1578
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S1319157816300854
Collections
  • Departament de Ciències de la Computació - Articles de revista [1.148]
  • GPLN - Grup de Processament del Llenguatge Natural - Articles de revista [97]
  View UPCommons Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
1-s2.0-S1319157816300854-main.pdf909,1KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Metadata under:Metadata under CC0
  • Contact Us
  • Send Feedback
  • Privacy Settings
  • Inici de la pàgina