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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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dc.contributor.author Giménez, Maite es_ES
dc.contributor.author Palanca Cámara, Javier es_ES
dc.contributor.author Botti Navarro, Vicente Juan es_ES
dc.date.accessioned 2021-05-27T03:35:24Z
dc.date.available 2021-05-27T03:35:24Z
dc.date.issued 2020-02-22 es_ES
dc.identifier.issn 0925-2312 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166844
dc.description 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 es_ES
dc.description.abstract [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 for having a fixed-size input matrix in a Convolutional Network effectively, using words present in the sentence. The methodology proposed has been evaluated intensively in Sentiment Analysis tasks using a variety of word embeddings. In all the experimentation carried out the proposed semantic-based padding improved the results achieved when no padding strategy is applied. Moreover, when the model used a pre-trained word embeddings, the performance of the state of the art has been surpassed. es_ES
dc.description.sponsorship 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 de Valencia. This work is partially supported by and grantnumber. the Grant PROMETEO/2018/002 from GVA. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Neurocomputing es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Natural language processing es_ES
dc.subject Convolutional neural networks es_ES
dc.subject Padding es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.neucom.2019.08.096 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-01-2461-2015/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002/ES/TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.neucom.2019.08.096 es_ES
dc.description.upvformatpinicio 315 es_ES
dc.description.upvformatpfin 323 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 378 es_ES
dc.relation.pasarela S\400125 es_ES
dc.contributor.funder Nvidia es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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