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http://hdl.handle.net/1843/66341| Tipo: | Artigo de Evento |
| Título: | PUCRJ-PUCPR-UFMG at eHealth-KD Challenge 2021: a multilingual BERT-based system for joint entity recognition and relation extraction |
| Autor(es): | Thiago Castro Ferreira João Vitor Andrioli de Souza Lucas Ferro Antunes de Oliveira Lucas Emanuel Silva e Oliveira Claudia Moro Barra Emerson Cabrera Paraíso Yohan Bonescki Gumiel Adriana Silvina Pagano Giovanni Pazini Meneghel Paiva Elisa Terumi Rubel Schneider Lucas Pavanelli |
| Resumen: | This 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 available |
| Asunto: | Processamento da linguagem natural (Computação) Informática na medicina |
| Idioma: | eng |
| País: | Brasil |
| Editor: | Universidade Federal de Minas Gerais |
| Sigla da Institución: | UFMG |
| Departamento: | FALE - FACULDADE DE LETRAS |
| Tipo de acceso: | Acesso Aberto |
| URI: | http://hdl.handle.net/1843/66341 |
| Fecha del documento: | 2021 |
| metadata.dc.url.externa: | https://ceur-ws.org/Vol-2943/ehealth_paper3.pdf |
| metadata.dc.relation.ispartof: | Iberian Languages Evaluation Forum |
| Aparece en las colecciones: | Artigo de Evento |
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| archivo | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| PUCRJ-PUCPR-UFMG at eHealth-KD Challenge 2021 a multilingual BERT-based system for joint entity recognition and relation extraction.pdf | 605.23 kB | Adobe PDF | Visualizar/Abrir |
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