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A Knowledge-Based Weighted KNN for Detecting Irony in Twitter

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A Knowledge-Based Weighted KNN for Detecting Irony in Twitter

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dc.contributor.author Hernandez-Farias, Delia Irazu es_ES
dc.contributor.author Montes Gomez, Manuel es_ES
dc.contributor.author Escalante, Hugo es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.contributor.author Patti, Viviana es_ES
dc.date.accessioned 2020-06-13T03:32:32Z
dc.date.available 2020-06-13T03:32:32Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/146278
dc.description.abstract [EN] In this work, we propose a variant of a well-known instancebased algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a challenging text classification task: irony detection. We experimented with corpora in the state of the art. The obtained results show that despite being a simple approach, our method is competitive with respect to more advanced techniques. es_ES
dc.description.sponsorship This research was funded by CONACYT project FC 2016-2410. The work of P. Rosso has been funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. The work of V. Patti was partially funded by Progetto di Ateneo/CSP 2016 (IhatePrejudice, S1618_L2_BOSC_01). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Instance-based algorithm es_ES
dc.subject WKNN es_ES
dc.subject Irony detection es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Knowledge-Based Weighted KNN for Detecting Irony in Twitter es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-030-04497-8_16 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONACyT//FC 2016-2410/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-71147-C2-1-P/ES/COMPRENSION DEL LENGUAJE EN LOS MEDIOS DE COMUNICACION SOCIAL - REPRESENTANDO CONTEXTOS DE FORMA CONTINUA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UNITO//S1618_L2_BOSC_01/ 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 Hernandez-Farias, DI.; Montes Gomez, M.; Escalante, H.; Rosso, P.; Patti, V. (2018). A Knowledge-Based Weighted KNN for Detecting Irony in Twitter. Lecture Notes in Computer Science. 11289:1-13. https://doi.org/10.1007/978-3-030-04497-8_16 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 17th Mexican International Conference on Artificial Intelligence (MICAI 2018) es_ES
dc.relation.conferencedate Octubre 22-27,2018 es_ES
dc.relation.conferenceplace Guadalajara, Mexico es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-04497-8_16 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11289 es_ES
dc.relation.pasarela S\384892 es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
dc.contributor.funder Università degli Studi di Torino es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Consejo Nacional de Ciencia y Tecnología, México es_ES
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