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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/66341
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dc.creatorThiago Castro Ferreirapt_BR
dc.creatorJoão Vitor Andrioli de Souzapt_BR
dc.creatorLucas Ferro Antunes de Oliveirapt_BR
dc.creatorLucas Emanuel Silva e Oliveirapt_BR
dc.creatorClaudia Moro Barrapt_BR
dc.creatorEmerson Cabrera Paraísopt_BR
dc.creatorYohan Bonescki Gumielpt_BR
dc.creatorAdriana Silvina Paganopt_BR
dc.creatorGiovanni Pazini Meneghel Paivapt_BR
dc.creatorElisa Terumi Rubel Schneiderpt_BR
dc.creatorLucas Pavanellipt_BR
dc.date.accessioned2024-03-21T18:20:27Z-
dc.date.available2024-03-21T18:20:27Z-
dc.date.issued2021-
dc.citation.spage683pt_BR
dc.citation.epage691pt_BR
dc.identifier.issn1613-0073pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/66341-
dc.description.resumoThis study introduces the system submitted to the eHealthKD Challenge 2021 by the PUCRJ-PUCPR-UFMG team. We proposed a multilingual BERT-based system for joint entity recognition and relation extraction in multidomain texts. Our end-to-end multitasking model benefits from the transformer architecture, which has proved to capture better the global dependencies of the input text. Also, the use of a multilingual model contributed to our system to perform well even in the set of tests containing non-Spanish sentences. Our system ranked first in the entity recognition task and second in the Main scenario, where both tasks of entity recognition and relation extraction had to be solved. The full code of our approach and more details of the implementation are publicly availablept_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentFALE - FACULDADE DE LETRASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofIberian Languages Evaluation Forumpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjecteHealthpt_BR
dc.subjectEntity recognitionpt_BR
dc.subjectRelation extractionpt_BR
dc.subjectBERTpt_BR
dc.subjectDeep learningpt_BR
dc.subject.otherProcessamento da linguagem natural (Computação)pt_BR
dc.subject.otherInformática na medicinapt_BR
dc.titlePUCRJ-PUCPR-UFMG at eHealth-KD Challenge 2021: a multilingual BERT-based system for joint entity recognition and relation extractionpt_BR
dc.typeArtigo de Eventopt_BR
dc.url.externahttps://ceur-ws.org/Vol-2943/ehealth_paper3.pdfpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-0200-3646pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-8950-0890pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4052-7993pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-8921-5598pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2228-7965pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-3150-3503pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-6740-7855pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2637-3086pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1811-5087pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-9789-9547pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-8239-2930pt_BR
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