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Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis

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Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis

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Giménez, M.; Palanca Cámara, J.; Botti Navarro, VJ. (2020). Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing. 378:315-323. https://doi.org/10.1016/j.neucom.2019.08.096

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Metadatos del ítem

Título: Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis
Autor: Giménez, Maite Palanca Cámara, Javier Botti Navarro, Vicente Juan
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] In this work, a methodology for applying semantic-based padding in Convolutional Neural Networks for Natural Language Processing tasks is proposed. Semantic-based padding takes advantage of the unused space required ...[+]
Palabras clave: Natural language processing , Convolutional neural networks , Padding
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Neurocomputing. (issn: 0925-2312 )
DOI: 10.1016/j.neucom.2019.08.096
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.neucom.2019.08.096
Código del Proyecto:
info:eu-repo/grantAgreement/UPV//PAID-01-2461-2015/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002/ES/TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/
Descripción: This is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Volume 378, 22 February 2020, DOI: 10.1016/j.neucom.2019.08.096
Agradecimientos:
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. The work of the first author is financed by Grant PAID-01-2461 2015, from the Universitat Politecnica ...[+]
Tipo: Artículo

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