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dc.contributor.authorStrzyz, Michalina
dc.contributor.authorVilares, David
dc.contributor.authorGómez-Rodríguez, Carlos
dc.date.accessioned2024-01-24T07:52:21Z
dc.date.available2024-01-24T07:52:21Z
dc.date.issued2019-06
dc.identifier.citationMichalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez. 2019. Viable Dependency Parsing as Sequence Labeling. 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 717–723, Minneapolis, Minnesota. Association for Computational Linguistics. doi: 10.18653/v1/N19-1077es_ES
dc.identifier.urihttp://hdl.handle.net/2183/35098
dc.description.abstract[Abstract]: We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BILSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.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 the TELEPARESUDC project (FFI2014-51978-C2-2-R) and the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01). We gratefully acknowledge NVIDIA Corporation for the donation of a GTX Titan X GPU.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/N19-1077es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDependency parsinges_ES
dc.subjectDependency treeses_ES
dc.subjectEncodingses_ES
dc.subjectParsing algorithmes_ES
dc.subjectSequence Labelinges_ES
dc.subjectSpeed accuracyes_ES
dc.subjectTreebankses_ES
dc.subjectComputational linguisticses_ES
dc.titleViable Dependency Parsing as Sequence Labelinges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.volume1es_ES
UDC.startPage717es_ES
UDC.endPage723es_ES
dc.identifier.doi10.18653/v1/N19-1077
UDC.conferenceTitleNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologieses_ES


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