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Viable Dependency Parsing as Sequence Labeling
dc.contributor.author | Strzyz, Michalina | |
dc.contributor.author | Vilares, David | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-01-24T07:52:21Z | |
dc.date.available | 2024-01-24T07:52:21Z | |
dc.date.issued | 2019-06 | |
dc.identifier.citation | Michalina 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/N19-1077 | es_ES |
dc.identifier.uri | http://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.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 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.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/N19-1077 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Dependency parsing | es_ES |
dc.subject | Dependency trees | es_ES |
dc.subject | Encodings | es_ES |
dc.subject | Parsing algorithm | es_ES |
dc.subject | Sequence Labeling | es_ES |
dc.subject | Speed accuracy | es_ES |
dc.subject | Treebanks | es_ES |
dc.subject | Computational linguistics | es_ES |
dc.title | Viable Dependency Parsing as Sequence Labeling | 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.volume | 1 | es_ES |
UDC.startPage | 717 | es_ES |
UDC.endPage | 723 | es_ES |
dc.identifier.doi | 10.18653/v1/N19-1077 | |
UDC.conferenceTitle | NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | es_ES |
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