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dc.contributor.authorFernández-González, Daniel
dc.contributor.authorGómez-Rodríguez, Carlos
dc.date.accessioned2024-01-23T13:38:31Z
dc.date.available2024-01-23T13:38:31Z
dc.date.issued2019
dc.identifier.citationDaniel Fernández-González and Carlos Gómez-Rodríguez. 2019. Left-to-Right Dependency Parsing with Pointer Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 710–716, Minneapolis, Minnesota. Association for Computational Linguistics. doi: 10.18653/v1/N19-1076es_ES
dc.identifier.urihttp://hdl.handle.net/2183/35082
dc.descriptionProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)es_ES
dc.description.abstract[Abstract]: We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.es_ES
dc.description.sponsorshipThis work has received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from MINECO (FFI2014-51978-C2-2-R, TIN2017-85160-C2-1-R) and from Xunta de Galicia (ED431B 2017/01).es_ES
dc.description.sponsorshipXunta de Galicia; ED431B 2017/01es_ES
dc.language.isoenges_ES
dc.publisherAssociation for Computational Linguistics (ACL)es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85160-C2-1-R/ES/AVANCES EN NUEVOS SISTEMAS DE EXTRACCION DE RESPUESTAS CON ANALISIS SEMANTICO Y APRENDIZAJE PROFUNDO/es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FFI2014-51978-C2-2-R/ES/TECNOLOGIAS DE LA LENGUA PARA ANALISIS DE OPINIONES EN REDES SOCIALES: DEL TEXTO AL MICROTEXTOes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/714150es_ES
dc.relation.urihttps://doi.org/10.18653/v1/N19-1076es_ES
dc.rightsCreative Commons Atribución 4.0 International.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectSyntacticses_ES
dc.subjectNetwork frameworkses_ES
dc.subjectRight dependencieses_ES
dc.subjectSingle modelses_ES
dc.subjectStack pointerses_ES
dc.subjectTopdownes_ES
dc.subjectTransition sequenceses_ES
dc.subjectTransition systemes_ES
dc.subjectComputational linguisticses_ES
dc.titleLeft-to-Right Dependency Parsing with Pointer Networkses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.startPage710es_ES
UDC.endPage716es_ES
dc.identifier.doi10.18653/v1/N19-1076
UDC.conferenceTitle2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologieses_ES


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