Using machine learning algorithms to identify named entities in legal documents: a preliminary approach

Detalhes bibliográficos
Autor(a) principal: Poudyal, Prakash
Data de Publicação: 2011
Outros Autores: Borrego, Luís, Quaresma, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/4899
Resumo: This paper deals with accuracy and performance of var- ious machine learning algorithms in the recognition and extraction of different types of named entities such as date, organization, reg- ulation laws and person. The experiment is based on 20 judicial decision documents from European Lex site. The obtained results were proposed for the selection of the best algorithm that selects appropriate maximum entities from the legal documents. To ver- ify the performance of algorithm, obtained data from the tagging entities were compared with manual work as reference.
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spelling Using machine learning algorithms to identify named entities in legal documents: a preliminary approachnamed entities recognitionmachine learningThis paper deals with accuracy and performance of var- ious machine learning algorithms in the recognition and extraction of different types of named entities such as date, organization, reg- ulation laws and person. The experiment is based on 20 judicial decision documents from European Lex site. The obtained results were proposed for the selection of the best algorithm that selects appropriate maximum entities from the legal documents. To ver- ify the performance of algorithm, obtained data from the tagging entities were compared with manual work as reference.Escola de Ciências e Tecnologia da Universidade de Évora2012-02-02T17:00:46Z2012-02-022011-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/4899http://hdl.handle.net/10174/4899engPrakash Poudyal, Luis Borrego e Paulo Quaresma. Using machine learning algorithms to identify named entities in legal documents: a preliminary approach. In JIUE'2011 - 2as Jornadas de Informática da Universidade de Évora. Évora, Portugal, pages 33-38. ISBN: 978-989-97060-2-6.prakash@di.uevora.ptluis.borrego@hotmail.pq@di.uevora.pt283Poudyal, PrakashBorrego, LuísQuaresma, Pauloinfo: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:RCAAP2024-01-03T18:42:34Zoai:dspace.uevora.pt:10174/4899Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:59:45.160500Repositório 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 Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
title Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
spellingShingle Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
Poudyal, Prakash
named entities recognition
machine learning
title_short Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
title_full Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
title_fullStr Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
title_full_unstemmed Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
title_sort Using machine learning algorithms to identify named entities in legal documents: a preliminary approach
author Poudyal, Prakash
author_facet Poudyal, Prakash
Borrego, Luís
Quaresma, Paulo
author_role author
author2 Borrego, Luís
Quaresma, Paulo
author2_role author
author
dc.contributor.author.fl_str_mv Poudyal, Prakash
Borrego, Luís
Quaresma, Paulo
dc.subject.por.fl_str_mv named entities recognition
machine learning
topic named entities recognition
machine learning
description This paper deals with accuracy and performance of var- ious machine learning algorithms in the recognition and extraction of different types of named entities such as date, organization, reg- ulation laws and person. The experiment is based on 20 judicial decision documents from European Lex site. The obtained results were proposed for the selection of the best algorithm that selects appropriate maximum entities from the legal documents. To ver- ify the performance of algorithm, obtained data from the tagging entities were compared with manual work as reference.
publishDate 2011
dc.date.none.fl_str_mv 2011-11-01T00:00:00Z
2012-02-02T17:00:46Z
2012-02-02
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/10174/4899
http://hdl.handle.net/10174/4899
url http://hdl.handle.net/10174/4899
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Prakash Poudyal, Luis Borrego e Paulo Quaresma. Using machine learning algorithms to identify named entities in legal documents: a preliminary approach. In JIUE'2011 - 2as Jornadas de Informática da Universidade de Évora. Évora, Portugal, pages 33-38. ISBN: 978-989-97060-2-6.
prakash@di.uevora.pt
luis.borrego@hotmail.
pq@di.uevora.pt
283
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Escola de Ciências e Tecnologia da Universidade de Évora
publisher.none.fl_str_mv Escola de Ciências e Tecnologia da Universidade de Évora
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
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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