Enhancing a Portuguese text classifier using part-of-speech tags
Autor(a) principal: | |
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Data de Publicação: | 2005 |
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/10174/2562 |
Resumo: | Support Vector Machines have been applied to text classification with great success. In this paper, we apply and evaluate the impact of using part-of- speech tags (nouns, proper nouns, adjectives and verbs) as a feature selection procedure in a European Portuguese written dataset – the Portuguese Attorney General’s Office documents. From the results, we can conclude that verbs alone don’t have enough informa- tion to produce good learners. On the other hand, we obtain learners with equiva- lent performance and a reduced number of features (at least half) if we use specific part-of-speech tags instead of all words. |
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Enhancing a Portuguese text classifier using part-of-speech tagsmachine learningText classificationSupport Vector Machines have been applied to text classification with great success. In this paper, we apply and evaluate the impact of using part-of- speech tags (nouns, proper nouns, adjectives and verbs) as a feature selection procedure in a European Portuguese written dataset – the Portuguese Attorney General’s Office documents. From the results, we can conclude that verbs alone don’t have enough informa- tion to produce good learners. On the other hand, we obtain learners with equiva- lent performance and a reduced number of features (at least half) if we use specific part-of-speech tags instead of all words.Springer-Verlag2011-02-15T11:39:29Z2011-02-152005-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article119604 bytesapplication/pdfhttp://hdl.handle.net/10174/2562http://hdl.handle.net/10174/2562eng189-198Advances in Soft Computinglivretcg@uevora.ptpq@uevora.ptIIPWM-05, Intelligent Information Processing and Web MiningKlopotek, M.Weirzchon, S.Trojanowski, K.606Gonçalves, 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:10174/2562Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:58:14.149072Repositório 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 |
Enhancing a Portuguese text classifier using part-of-speech tags |
title |
Enhancing a Portuguese text classifier using part-of-speech tags |
spellingShingle |
Enhancing a Portuguese text classifier using part-of-speech tags Gonçalves, Teresa machine learning Text classification |
title_short |
Enhancing a Portuguese text classifier using part-of-speech tags |
title_full |
Enhancing a Portuguese text classifier using part-of-speech tags |
title_fullStr |
Enhancing a Portuguese text classifier using part-of-speech tags |
title_full_unstemmed |
Enhancing a Portuguese text classifier using part-of-speech tags |
title_sort |
Enhancing a Portuguese text classifier using part-of-speech tags |
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 Text classification |
topic |
machine learning Text classification |
description |
Support Vector Machines have been applied to text classification with great success. In this paper, we apply and evaluate the impact of using part-of- speech tags (nouns, proper nouns, adjectives and verbs) as a feature selection procedure in a European Portuguese written dataset – the Portuguese Attorney General’s Office documents. From the results, we can conclude that verbs alone don’t have enough informa- tion to produce good learners. On the other hand, we obtain learners with equiva- lent performance and a reduced number of features (at least half) if we use specific part-of-speech tags instead of all words. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-01-01T00:00:00Z 2011-02-15T11:39:29Z 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/10174/2562 http://hdl.handle.net/10174/2562 |
url |
http://hdl.handle.net/10174/2562 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
189-198 Advances in Soft Computing livre tcg@uevora.pt pq@uevora.pt IIPWM-05, Intelligent Information Processing and Web Mining Klopotek, M. Weirzchon, S. Trojanowski, K. 606 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
119604 bytes application/pdf |
dc.publisher.none.fl_str_mv |
Springer-Verlag |
publisher.none.fl_str_mv |
Springer-Verlag |
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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 |
reponame_str |
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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