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

dc.contributor.author Quispe, Oscar es_PE
dc.contributor.author Ocsa, Alexander es_PE
dc.contributor.author Coronado, Ricardo es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2017-11
dc.description.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.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1109/la-cci.2017.8285711
dc.identifier.uri https://hdl.handle.net/20.500.12390/1302
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
dc.rights info:eu-repo/semantics/openAccess
dc.subject indexing
dc.subject convolution es_PE
dc.subject feature extraction es_PE
dc.subject feedforward neural nets es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#5.08.02
dc.title Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication
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