Keyword extracting using auto-associative neural networks

Autores
Aquino, Germán Osvaldo; Hasperué, Waldo; Lanzarini, Laura Cristina
Año de publicación
2014
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 a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as autoencoders, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document.
XI Workshop Bases de Datos y Minería de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
keyword extraction
text mining
neural networks
autoencoders
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:10915/42284
Acceder
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spelling Keyword extracting using auto-associative neural networksAquino, Germán OsvaldoHasperué, WaldoLanzarini, Laura CristinaCiencias Informáticaskeyword extractiontext miningneural networksautoencodersThe 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 a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as <i>autoencoders</i>, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras en 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/10915/42284enginfo: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:UNLP2025-11-05T12:41:15Zoai:sedici.unlp.edu.ar:10915/42284Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-05 12:41:15.86SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Keyword extracting using auto-associative neural networks
title Keyword extracting using auto-associative neural networks
spellingShingle Keyword extracting using auto-associative neural networks
Aquino, Germán Osvaldo
Ciencias Informáticas
keyword extraction
text mining
neural networks
autoencoders
title_short Keyword extracting using auto-associative neural networks
title_full Keyword extracting using auto-associative neural networks
title_fullStr Keyword extracting using auto-associative neural networks
title_full_unstemmed Keyword extracting using auto-associative neural networks
title_sort Keyword extracting using auto-associative neural networks
dc.creator.none.fl_str_mv Aquino, Germán Osvaldo
Hasperué, Waldo
Lanzarini, Laura Cristina
author Aquino, Germán Osvaldo
author_facet Aquino, Germán Osvaldo
Hasperué, Waldo
Lanzarini, Laura Cristina
author_role author
author2 Hasperué, Waldo
Lanzarini, Laura Cristina
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
keyword extraction
text mining
neural networks
autoencoders
topic Ciencias Informáticas
keyword extraction
text mining
neural networks
autoencoders
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 a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as <i>autoencoders</i>, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document.
XI Workshop Bases de Datos y Minería de Datos
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 a new algorithm for keyword extraction. Its main goal is to extract keywords from text documents written in Spanish quickly and without requiring a large training set. This goal was achieved using auto-associative neural networks, also known as <i>autoencoders</i>, trained using only the terms designated as keywords in the training set, so that these networks can learn the features characterizing the important terms in a document.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/42284
url http://sedici.unlp.edu.ar/handle/10915/42284
dc.language.none.fl_str_mv eng
language eng
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)
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instacron_str UNLP
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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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