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dc.contributor.advisorAgerri Gascón, Rodrigo ORCID
dc.contributor.authorYeginbergenova, Anar
dc.date.accessioned2023-07-03T15:38:10Z
dc.date.available2023-07-03T15:38:10Z
dc.date.issued2023-07-03
dc.identifier.urihttp://hdl.handle.net/10810/61853
dc.description.abstractNowadays the medical domain is receiving more and more attention in the applications involving Artificial Intelligence. Clinicians have to deal with an enormous amount of unstructured textual data to make a conclusion about patient's health in their everyday life. Argument mining helps to provide a structure to such data by detecting argumentative components in the text and classifying the relations between them. However, as it is the case for many tasks in Natural Language Processing in general and in medical text processing in particular, the large majority of the work on computational argumentation has been done only for English. This is also the case with the only dataset available for argumentation in the medical domain, namely, the annotated medical data of abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database. In order to mitigate the lack of annotated data for other languages, we empirically investigate several strategies to perform argument mining and classification in medical texts for a language for which no annotated data is available. This thesis shows that automatically translating and project annotations from English to a target language (Spanish) is an effective way to generate annotated data without manual intervention. Furthermore, our experiments demonstrate that the translation and projection approach outperforms zero-shot cross-lingual approaches using a large masked multilingual language model. Finally, we show how the automatically generated data in Spanish can also be used to improve results in the original English evaluation setting.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectargument mininges_ES
dc.titleCross-lingual argument mining in the medical domaines_ES
dc.typeinfo:eu-repo/semantics/masterThesis
dc.date.updated2022-06-14T10:20:27Z
dc.language.rfc3066es
dc.rights.holder© 2022, la autora
dc.contributor.degreeMáster Universitario Erasmus Mundus en Tecnologías del Lenguaje y la Comunicación (LCT)
dc.contributor.degreeHizkuntzaren eta Komunikazioaren Teknologiak Erasmus Mundus Unibertsitate Masterra (LCT)
dc.contributor.degreeErasmus Mundus Master in Language and Communication Technologies (LCT)
dc.identifier.gaurregister123297-1094939-09es_ES
dc.identifier.gaurassign137891-1094939es_ES


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