Keyword Identification in Spanish Documents using Neural Networks

Autores
Aquino, Germán Osvaldo; Lanzarini, Laura Cristina
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.
XII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
keyword extraction
autoencoders
Neural nets
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:10915/50434
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/50434
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Keyword Identification in Spanish Documents using Neural NetworksAquino, Germán OsvaldoLanzarini, Laura CristinaCiencias Informáticaskeyword extractionautoencodersNeural netsThe large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.XII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI)2015-10info: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/10915/50434enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6info:eu-repo/semantics/reference/hdl/10915/50028info: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:UNLP2025-10-15T10:56:28Zoai:sedici.unlp.edu.ar:10915/50434Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:56:28.831SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Keyword Identification in Spanish Documents using Neural Networks
title Keyword Identification in Spanish Documents using Neural Networks
spellingShingle Keyword Identification in Spanish Documents using Neural Networks
Aquino, Germán Osvaldo
Ciencias Informáticas
keyword extraction
autoencoders
Neural nets
title_short Keyword Identification in Spanish Documents using Neural Networks
title_full Keyword Identification in Spanish Documents using Neural Networks
title_fullStr Keyword Identification in Spanish Documents using Neural Networks
title_full_unstemmed Keyword Identification in Spanish Documents using Neural Networks
title_sort Keyword Identification in Spanish Documents using Neural Networks
dc.creator.none.fl_str_mv Aquino, Germán Osvaldo
Lanzarini, Laura Cristina
author Aquino, Germán Osvaldo
author_facet Aquino, Germán Osvaldo
Lanzarini, Laura Cristina
author_role author
author2 Lanzarini, Laura Cristina
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
keyword extraction
autoencoders
Neural nets
topic Ciencias Informáticas
keyword extraction
autoencoders
Neural nets
dc.description.none.fl_txt_mv The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.
XII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
description The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/50434
url http://sedici.unlp.edu.ar/handle/10915/50434
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6
info:eu-repo/semantics/reference/hdl/10915/50028
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
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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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