A novel, Language-Independent Keyword Extraction method

Autores
Aquino, Germán Osvaldo; Hasperué, Waldo; Estrebou, César Armando; Lanzarini, Laura Cristina
Año de publicación
2013
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Obtaining the most representative set of words in a document is a very significant task, since it allows characterizing the document and simplifies search and classification activities. This paper presents a novel method, called LIKE, that offers the ability of automatically extracting keywords from a document regardless of the language used in it. To do so, it uses a three-stage process: the first stage identifies the most representative terms, the second stage builds a numeric representation that is appropriate for those terms, and the third one uses a feed-forward neural network to obtain a predictive model. To measure the efficacy of the LIKE method, the articles published by the Workshop of Computer Science Researchers (WICC) in the last 14 years (1999-2012) were used. The results obtained show that LIKE is better than the KEA method, which is one of the most widely mentioned solutions in literature about this topic.
X Workshop bases de datos y minería de datos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
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:10915/31256
Acceder
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network_name_str SEDICI (UNLP)
spelling A novel, Language-Independent Keyword Extraction methodAquino, Germán OsvaldoHasperué, WaldoEstrebou, César ArmandoLanzarini, Laura CristinaCiencias InformáticasData miningDATABASE MANAGEMENTtext miningdocument characterizationback-propagationObtaining the most representative set of words in a document is a very significant task, since it allows characterizing the document and simplifies search and classification activities. This paper presents a novel method, called LIKE, that offers the ability of automatically extracting keywords from a document regardless of the language used in it. To do so, it uses a three-stage process: the first stage identifies the most representative terms, the second stage builds a numeric representation that is appropriate for those terms, and the third one uses a feed-forward neural network to obtain a predictive model. To measure the efficacy of the LIKE method, the articles published by the Workshop of Computer Science Researchers (WICC) in the last 14 years (1999-2012) were used. The results obtained show that LIKE is better than the KEA method, which is one of the most widely mentioned solutions in literature about this topic.X Workshop bases de datos y minería de datosRed de Universidades con Carreras en Informática (RedUNCI)2013-10info: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/10915/31256spainfo: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:UNLP2025-09-03T10:30:38Zoai:sedici.unlp.edu.ar:10915/31256Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:30:38.484SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A novel, Language-Independent Keyword Extraction method
title A novel, Language-Independent Keyword Extraction method
spellingShingle A novel, Language-Independent Keyword Extraction method
Aquino, Germán Osvaldo
Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
title_short A novel, Language-Independent Keyword Extraction method
title_full A novel, Language-Independent Keyword Extraction method
title_fullStr A novel, Language-Independent Keyword Extraction method
title_full_unstemmed A novel, Language-Independent Keyword Extraction method
title_sort A novel, Language-Independent Keyword Extraction method
dc.creator.none.fl_str_mv Aquino, Germán Osvaldo
Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
author Aquino, Germán Osvaldo
author_facet Aquino, Germán Osvaldo
Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
author_role author
author2 Hasperué, Waldo
Estrebou, César Armando
Lanzarini, Laura Cristina
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
topic Ciencias Informáticas
Data mining
DATABASE MANAGEMENT
text mining
document characterization
back-propagation
dc.description.none.fl_txt_mv Obtaining the most representative set of words in a document is a very significant task, since it allows characterizing the document and simplifies search and classification activities. This paper presents a novel method, called LIKE, that offers the ability of automatically extracting keywords from a document regardless of the language used in it. To do so, it uses a three-stage process: the first stage identifies the most representative terms, the second stage builds a numeric representation that is appropriate for those terms, and the third one uses a feed-forward neural network to obtain a predictive model. To measure the efficacy of the LIKE method, the articles published by the Workshop of Computer Science Researchers (WICC) in the last 14 years (1999-2012) were used. The results obtained show that LIKE is better than the KEA method, which is one of the most widely mentioned solutions in literature about this topic.
X Workshop bases de datos y minería de datos
Red de Universidades con Carreras en Informática (RedUNCI)
description Obtaining the most representative set of words in a document is a very significant task, since it allows characterizing the document and simplifies search and classification activities. This paper presents a novel method, called LIKE, that offers the ability of automatically extracting keywords from a document regardless of the language used in it. To do so, it uses a three-stage process: the first stage identifies the most representative terms, the second stage builds a numeric representation that is appropriate for those terms, and the third one uses a feed-forward neural network to obtain a predictive model. To measure the efficacy of the LIKE method, the articles published by the Workshop of Computer Science Researchers (WICC) in the last 14 years (1999-2012) were used. The results obtained show that LIKE is better than the KEA method, which is one of the most widely mentioned solutions in literature about this topic.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/31256
url http://sedici.unlp.edu.ar/handle/10915/31256
dc.language.none.fl_str_mv spa
language spa
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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