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Towards a Universal Semantic Dictionary

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Towards a Universal Semantic Dictionary

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dc.contributor.author Castro-Bleda, Maria Jose es_ES
dc.contributor.author Iklódi, E. es_ES
dc.contributor.author Recski, G. es_ES
dc.contributor.author Borbély, G. es_ES
dc.date.accessioned 2020-03-23T08:46:16Z
dc.date.available 2020-03-23T08:46:16Z
dc.date.issued 2019-10 es_ES
dc.identifier.uri http://hdl.handle.net/10251/139157
dc.description.abstract [EN] A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, multilingual embedding space, is proposed in this paper. Previous approaches learned translation matrices between two specific languages, while this method learns translation matrices between a given language and a shared, multilingual space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case, two different training data were applied: Dinu¿s English¿Italian benchmark data, and English¿Italian translation pairs extracted from the PanLex database. In the second case, only the PanLex database was used. The system performs on English¿Italian languages with the best setting significantly better than the baseline system given by Mikolov, and it provides a comparable performance with more sophisticated systems. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages. es_ES
dc.description.sponsorship This research was funded by Spanish MINECO and FEDER grant number TIN2017-85854-C4-2-R. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Natural language processing es_ES
dc.subject Semantics es_ES
dc.subject Word embeddings es_ES
dc.subject Multilingual embeddings es_ES
dc.subject Translation es_ES
dc.subject Artificial neural networks es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Towards a Universal Semantic Dictionary es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app9194060 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85854-C4-2-R/ES/AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL/ 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 Castro-Bleda, MJ.; Iklódi, E.; Recski, G.; Borbély, G. (2019). Towards a Universal Semantic Dictionary. Applied Sciences. 9(19):1-14. https://doi.org/10.3390/app9194060 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app9194060 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 19 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\396624 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.description.references Youn, H., Sutton, L., Smith, E., Moore, C., Wilkins, J. F., Maddieson, I., … Bhattacharya, T. (2016). On the universal structure of human lexical semantics. Proceedings of the National Academy of Sciences, 113(7), 1766-1771. doi:10.1073/pnas.1520752113 es_ES
dc.description.references Ruder, S., Vulić, I., & Søgaard, A. (2019). A Survey of Cross-lingual Word Embedding Models. Journal of Artificial Intelligence Research, 65, 569-631. doi:10.1613/jair.1.11640 es_ES
dc.description.references Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146. doi:10.1162/tacl_a_00051 es_ES


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