Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/2556 |
Resumo: | Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem. |
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Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documentsmachine learningnamed entity recognitionInformation extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem.Springer-Verlag2011-02-15T10:47:31Z2011-02-152010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article260319 bytesapplication/pdfhttp://hdl.handle.net/10174/2556http://hdl.handle.net/10174/2556eng44-59978-3-642-12836-3Lecture Notes in Computer Science6036livretcg@uevora.ptpq@uevora.ptSemantic Processing of Legal Texts498Gonçalves, TeresaQuaresma, 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:RCAAP2024-01-03T18:39:06Zoai:dspace.uevora.pt:10174/2556Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:58:14.062062Repositório 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 Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
title |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
spellingShingle |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents Gonçalves, Teresa machine learning named entity recognition |
title_short |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
title_full |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
title_fullStr |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
title_full_unstemmed |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
title_sort |
Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents |
author |
Gonçalves, Teresa |
author_facet |
Gonçalves, Teresa Quaresma, Paulo |
author_role |
author |
author2 |
Quaresma, Paulo |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Gonçalves, Teresa Quaresma, Paulo |
dc.subject.por.fl_str_mv |
machine learning named entity recognition |
topic |
machine learning named entity recognition |
description |
Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-01-01T00:00:00Z 2011-02-15T10:47:31Z 2011-02-15 |
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/10174/2556 http://hdl.handle.net/10174/2556 |
url |
http://hdl.handle.net/10174/2556 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
44-59 978-3-642-12836-3 Lecture Notes in Computer Science 6036 livre tcg@uevora.pt pq@uevora.pt Semantic Processing of Legal Texts 498 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
260319 bytes application/pdf |
dc.publisher.none.fl_str_mv |
Springer-Verlag |
publisher.none.fl_str_mv |
Springer-Verlag |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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