Arabic medical entity tagging using distant learning in a multilingual framework

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• dc.contributor.author Cotik, Viviana
• dc.contributor.author Rodríguez, Horacio
• dc.contributor.author Vivaldi, Jorge
• dc.date.accessioned 2023-12-11T06:46:34Z
• dc.date.available 2023-12-11T06:46:34Z
• dc.date.issued 2017
• dc.description.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.
• dc.description.sponsorship This work was partially supported by the TUNER project (Spanish Ministerio de Economía y Competitividad, TIN2015-65308-C5-5-R) and the GRAPH-MED project (Spanish Ministerio de Economía y Competitividad, TIN2016-77820-C3-3-R).
• dc.format.mimetype application/pdf
• dc.identifier.citation Cotik 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. 2017 Apr;29(2):204-11. DOI: 10.1016/j.jksuci.2016.10.004
• dc.identifier.doi http://dx.doi.org/10.1016/j.jksuci.2016.10.004
• dc.identifier.issn 1319-1578
• dc.identifier.uri http://hdl.handle.net/10230/58483
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Journal of King Saud University - Computer and Information Sciences. 2017 Apr;29(2):204-11
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2016-77820-C3-3-R
• dc.rights © 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Semantic tagging
• dc.subject.keyword Multilingual
• dc.subject.keyword Medical domain
• dc.subject.keyword Arabic natural
• dc.subject.keyword Language processing
• dc.title Arabic medical entity tagging using distant learning in a multilingual framework
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion