Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

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The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representat...

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Detalles Bibliográficos
Autores: Quispe, Oscar, Ocsa, Alexander, Coronado, Ricardo
Formato: objeto de conferencia
Fecha de Publicación:2017
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/1302
Enlace del recurso:https://hdl.handle.net/20.500.12390/1302
https://doi.org/10.1109/la-cci.2017.8285711
Nivel de acceso:acceso abierto
Materia:indexing
convolution
feature extraction
feedforward neural nets
https://purl.org/pe-repo/ocde/ford#5.08.02
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spelling Publicationrp03689500rp03688500rp03687500Quispe, OscarOcsa, AlexanderCoronado, Ricardo2024-05-30T23:13:38Z2024-05-30T23:13:38Z2017-11https://hdl.handle.net/20.500.12390/1302https://doi.org/10.1109/la-cci.2017.8285711The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEE2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)info:eu-repo/semantics/openAccessindexingconvolution-1feature extraction-1feedforward neural nets-1https://purl.org/pe-repo/ocde/ford#5.08.02-1Latent semantic indexing and convolutional neural network for multi-label and multi-class text classificationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/1302oai:repositorio.concytec.gob.pe:20.500.12390/13022024-05-30 15:52:09.66http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="42bc58e0-594f-4113-9d16-a2f8724e2be8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification</Title> <PublishedIn> <Publication> <Title>2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)</Title> </Publication> </PublishedIn> <PublicationDate>2017-11</PublicationDate> <DOI>https://doi.org/10.1109/la-cci.2017.8285711</DOI> <Authors> <Author> <DisplayName>Quispe, Oscar</DisplayName> <Person id="rp03689" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Ocsa, Alexander</DisplayName> <Person id="rp03688" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Coronado, Ricardo</DisplayName> <Person id="rp03687" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>indexing</Keyword> <Keyword>convolution</Keyword> <Keyword>feature extraction</Keyword> <Keyword>feedforward neural nets</Keyword> <Abstract>The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
title Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
spellingShingle Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
Quispe, Oscar
indexing
convolution
feature extraction
feedforward neural nets
https://purl.org/pe-repo/ocde/ford#5.08.02
title_short Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
title_full Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
title_fullStr Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
title_full_unstemmed Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
title_sort Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
author Quispe, Oscar
author_facet Quispe, Oscar
Ocsa, Alexander
Coronado, Ricardo
author_role author
author2 Ocsa, Alexander
Coronado, Ricardo
author2_role author
author
dc.contributor.author.fl_str_mv Quispe, Oscar
Ocsa, Alexander
Coronado, Ricardo
dc.subject.none.fl_str_mv indexing
topic indexing
convolution
feature extraction
feedforward neural nets
https://purl.org/pe-repo/ocde/ford#5.08.02
dc.subject.es_PE.fl_str_mv convolution
feature extraction
feedforward neural nets
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.08.02
description The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/1302
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/la-cci.2017.8285711
url https://hdl.handle.net/20.500.12390/1302
https://doi.org/10.1109/la-cci.2017.8285711
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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score 13.434648
Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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