Ideentifying featured articles in Spanish Wikipedia

Autores
Pohn, Lian; Ferretti, Edgardo; Errecalde, Marcelo Luis
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Information Quality assessment in Wikipedia has become an ever-growing research line in the last years. However, few e orts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present the rst study to automatically assess information quality in Spanish Wikipedia, where Featured Articles identi cation is evaluated as a binary classi cation task. Two popular classi cation approaches like Naive Bayes and Support Vector Machine (SVM) are evaluated with di erent document representations and vocabulary sizes. The obtained results show that FA identi cation can be performed with an F1 score of 0.81, when SVM is used as classi cation algorithm and documents are represented with a binary codi cation of the bag-of-words model with reduced vocabulary.
XI Workshop Bases de Datos y Minería de Datos
Red de Universidades con Carreras de Informática (RedUNCI)
Materia
Ciencias Informáticas
Wikipedia
information quality
featured article
support vector machine
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42288
Acceder
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spelling Ideentifying featured articles in Spanish WikipediaPohn, LianFerretti, EdgardoErrecalde, Marcelo LuisCiencias InformáticasWikipediainformation qualityfeatured articlesupport vector machineInformation Quality assessment in Wikipedia has become an ever-growing research line in the last years. However, few e orts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present the rst study to automatically assess information quality in Spanish Wikipedia, where Featured Articles identi cation is evaluated as a binary classi cation task. Two popular classi cation approaches like Naive Bayes and Support Vector Machine (SVM) are evaluated with di erent document representations and vocabulary sizes. The obtained results show that FA identi cation can be performed with an F1 score of 0.81, when SVM is used as classi cation algorithm and documents are represented with a binary codi cation of the bag-of-words model with reduced vocabulary.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI)2014-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/42288enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:01:23Zoai:sedici.unlp.edu.ar:10915/42288Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:01:23.214SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Ideentifying featured articles in Spanish Wikipedia
title Ideentifying featured articles in Spanish Wikipedia
spellingShingle Ideentifying featured articles in Spanish Wikipedia
Pohn, Lian
Ciencias Informáticas
Wikipedia
information quality
featured article
support vector machine
title_short Ideentifying featured articles in Spanish Wikipedia
title_full Ideentifying featured articles in Spanish Wikipedia
title_fullStr Ideentifying featured articles in Spanish Wikipedia
title_full_unstemmed Ideentifying featured articles in Spanish Wikipedia
title_sort Ideentifying featured articles in Spanish Wikipedia
dc.creator.none.fl_str_mv Pohn, Lian
Ferretti, Edgardo
Errecalde, Marcelo Luis
author Pohn, Lian
author_facet Pohn, Lian
Ferretti, Edgardo
Errecalde, Marcelo Luis
author_role author
author2 Ferretti, Edgardo
Errecalde, Marcelo Luis
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Wikipedia
information quality
featured article
support vector machine
topic Ciencias Informáticas
Wikipedia
information quality
featured article
support vector machine
dc.description.none.fl_txt_mv Information Quality assessment in Wikipedia has become an ever-growing research line in the last years. However, few e orts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present the rst study to automatically assess information quality in Spanish Wikipedia, where Featured Articles identi cation is evaluated as a binary classi cation task. Two popular classi cation approaches like Naive Bayes and Support Vector Machine (SVM) are evaluated with di erent document representations and vocabulary sizes. The obtained results show that FA identi cation can be performed with an F1 score of 0.81, when SVM is used as classi cation algorithm and documents are represented with a binary codi cation of the bag-of-words model with reduced vocabulary.
XI Workshop Bases de Datos y Minería de Datos
Red de Universidades con Carreras de Informática (RedUNCI)
description Information Quality assessment in Wikipedia has become an ever-growing research line in the last years. However, few e orts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present the rst study to automatically assess information quality in Spanish Wikipedia, where Featured Articles identi cation is evaluated as a binary classi cation task. Two popular classi cation approaches like Naive Bayes and Support Vector Machine (SVM) are evaluated with di erent document representations and vocabulary sizes. The obtained results show that FA identi cation can be performed with an F1 score of 0.81, when SVM is used as classi cation algorithm and documents are represented with a binary codi cation of the bag-of-words model with reduced vocabulary.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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