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Left-to-Right Dependency Parsing with Pointer Networks
dc.contributor.author | Fernández-González, Daniel | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-01-23T13:38:31Z | |
dc.date.available | 2024-01-23T13:38:31Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Daniel 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/N19-1076 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/35082 | |
dc.description | 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) | 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.sponsorship | This 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.sponsorship | Xunta de Galicia; ED431B 2017/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics (ACL) | es_ES |
dc.relation | info: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.relation | info: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 MICROTEXTO | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/714150 | es_ES |
dc.relation.uri | https://doi.org/10.18653/v1/N19-1076 | es_ES |
dc.rights | Creative Commons Atribución 4.0 International. | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Syntactics | es_ES |
dc.subject | Network frameworks | es_ES |
dc.subject | Right dependencies | es_ES |
dc.subject | Single models | es_ES |
dc.subject | Stack pointers | es_ES |
dc.subject | Topdown | es_ES |
dc.subject | Transition sequences | es_ES |
dc.subject | Transition system | es_ES |
dc.subject | Computational linguistics | es_ES |
dc.title | Left-to-Right Dependency Parsing with Pointer Networks | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 710 | es_ES |
UDC.endPage | 716 | es_ES |
dc.identifier.doi | 10.18653/v1/N19-1076 | |
UDC.conferenceTitle | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | es_ES |
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