Related Named Entities Classification in the Economic-Financial Context

Detalhes bibliográficos
Autor(a) principal: De Los Reyes, Daniel
Data de Publicação: 2021
Outros Autores: Barcelos, Allan, Vieira, Renata, Manssour, Isabel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/29885
Resumo: The present work uses the Bidirectional Encoder Representations from Transformers(BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.
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spelling Related Named Entities Classification in the Economic-Financial ContextNamed EntitiesInformation ExtractionThe present work uses the Bidirectional Encoder Representations from Transformers(BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.FCT CEECIND/01997/2017, UIDB/00057/2020ACL Anthology2021-06-08T11:02:44Z2021-06-082021-04-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/29885http://hdl.handle.net/10174/29885engDe Los Reyes, D., Barcelos, A., Vieira, R., Manssour, I. Related Named Entities Classification in the Economic-Financial Context. Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation. 2021. ACL Anthology.https://www.aclweb.org/anthology/2021.hackashop-1.0/ndndrenatav@uevora.ptnd498De Los Reyes, DanielBarcelos, AllanVieira, RenataManssour, Isabelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:26:56Zoai:dspace.uevora.pt:10174/29885Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:19:16.626694Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Related Named Entities Classification in the Economic-Financial Context
title Related Named Entities Classification in the Economic-Financial Context
spellingShingle Related Named Entities Classification in the Economic-Financial Context
De Los Reyes, Daniel
Named Entities
Information Extraction
title_short Related Named Entities Classification in the Economic-Financial Context
title_full Related Named Entities Classification in the Economic-Financial Context
title_fullStr Related Named Entities Classification in the Economic-Financial Context
title_full_unstemmed Related Named Entities Classification in the Economic-Financial Context
title_sort Related Named Entities Classification in the Economic-Financial Context
author De Los Reyes, Daniel
author_facet De Los Reyes, Daniel
Barcelos, Allan
Vieira, Renata
Manssour, Isabel
author_role author
author2 Barcelos, Allan
Vieira, Renata
Manssour, Isabel
author2_role author
author
author
dc.contributor.author.fl_str_mv De Los Reyes, Daniel
Barcelos, Allan
Vieira, Renata
Manssour, Isabel
dc.subject.por.fl_str_mv Named Entities
Information Extraction
topic Named Entities
Information Extraction
description The present work uses the Bidirectional Encoder Representations from Transformers(BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-08T11:02:44Z
2021-06-08
2021-04-19T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/29885
http://hdl.handle.net/10174/29885
url http://hdl.handle.net/10174/29885
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv De Los Reyes, D., Barcelos, A., Vieira, R., Manssour, I. Related Named Entities Classification in the Economic-Financial Context. Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation. 2021. ACL Anthology.
https://www.aclweb.org/anthology/2021.hackashop-1.0/
nd
nd
renatav@uevora.pt
nd
498
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv ACL Anthology
publisher.none.fl_str_mv ACL Anthology
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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