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Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach

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Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach

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dc.contributor.author Banerjee, Somnath es_ES
dc.contributor.author Kumar Naskar, Sudip es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.contributor.author Bandyopadhyay, Sivaji es_ES
dc.date.accessioned 2019-05-19T20:02:35Z
dc.date.available 2019-05-19T20:02:35Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1064-1246 es_ES
dc.identifier.uri http://hdl.handle.net/10251/120700
dc.description.abstract [EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions. es_ES
dc.description.sponsorship The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. es_ES
dc.language Inglés es_ES
dc.publisher IOS Press es_ES
dc.relation MINECO/TIN2015-71147-C2-1-P es_ES
dc.relation.ispartof Journal of Intelligent & Fuzzy Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Question answering es_ES
dc.subject Code-mixing es_ES
dc.subject Cross-scripting es_ES
dc.subject Question classification es_ES
dc.subject Deep learning es_ES
dc.subject Social media content es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3233/JIFS-169481 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 Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3233/JIFS-169481 es_ES
dc.description.upvformatpinicio 2959 es_ES
dc.description.upvformatpfin 2969 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 34 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela S\384156 es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES


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