An efficient Particle Swarm Optimization approach to cluster short texts

Autores
Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Ingaramo, Diego; Rosso, Paolo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO★, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO★ is an effective clustering method for short-text corpora of small and medium size.
Fil: Cagnina, Leticia Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Ingaramo, Diego. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Rosso, Paolo. Universidad Politécnica de Valencia; España
Materia
Particle Swarm Optimization
Short-Text Clustering
Clustering as Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/32058
Acceder
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network_name_str CONICET Digital (CONICET)
spelling An efficient Particle Swarm Optimization approach to cluster short textsCagnina, Leticia CeciliaErrecalde, Marcelo LuisIngaramo, DiegoRosso, PaoloParticle Swarm OptimizationShort-Text ClusteringClustering as Optimizationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO★, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO★ is an effective clustering method for short-text corpora of small and medium size.Fil: Cagnina, Leticia Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Ingaramo, Diego. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Rosso, Paolo. Universidad Politécnica de Valencia; EspañaElsevier2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/32058Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Ingaramo, Diego; Rosso, Paolo; An efficient Particle Swarm Optimization approach to cluster short texts; Elsevier; Information Sciences; 265; 5-2014; 36-490020-0255CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2013.12.010info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0020025513008542info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:59:53Zoai:ri.conicet.gov.ar:11336/32058instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:59:53.715CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An efficient Particle Swarm Optimization approach to cluster short texts
title An efficient Particle Swarm Optimization approach to cluster short texts
spellingShingle An efficient Particle Swarm Optimization approach to cluster short texts
Cagnina, Leticia Cecilia
Particle Swarm Optimization
Short-Text Clustering
Clustering as Optimization
title_short An efficient Particle Swarm Optimization approach to cluster short texts
title_full An efficient Particle Swarm Optimization approach to cluster short texts
title_fullStr An efficient Particle Swarm Optimization approach to cluster short texts
title_full_unstemmed An efficient Particle Swarm Optimization approach to cluster short texts
title_sort An efficient Particle Swarm Optimization approach to cluster short texts
dc.creator.none.fl_str_mv Cagnina, Leticia Cecilia
Errecalde, Marcelo Luis
Ingaramo, Diego
Rosso, Paolo
author Cagnina, Leticia Cecilia
author_facet Cagnina, Leticia Cecilia
Errecalde, Marcelo Luis
Ingaramo, Diego
Rosso, Paolo
author_role author
author2 Errecalde, Marcelo Luis
Ingaramo, Diego
Rosso, Paolo
author2_role author
author
author
dc.subject.none.fl_str_mv Particle Swarm Optimization
Short-Text Clustering
Clustering as Optimization
topic Particle Swarm Optimization
Short-Text Clustering
Clustering as Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO★, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO★ is an effective clustering method for short-text corpora of small and medium size.
Fil: Cagnina, Leticia Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Ingaramo, Diego. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Rosso, Paolo. Universidad Politécnica de Valencia; España
description Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO★, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO★ is an effective clustering method for short-text corpora of small and medium size.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/32058
Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Ingaramo, Diego; Rosso, Paolo; An efficient Particle Swarm Optimization approach to cluster short texts; Elsevier; Information Sciences; 265; 5-2014; 36-49
0020-0255
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32058
identifier_str_mv Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Ingaramo, Diego; Rosso, Paolo; An efficient Particle Swarm Optimization approach to cluster short texts; Elsevier; Information Sciences; 265; 5-2014; 36-49
0020-0255
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2013.12.010
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0020025513008542
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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