dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.contributor.author | Vilares, David | |
dc.date.accessioned | 2024-01-23T13:52:57Z | |
dc.date.available | 2024-01-23T13:52:57Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Carlos Gómez-Rodríguez and David Vilares. 2018. Constituent Parsing as Sequence Labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1314–1324, Brussels, Belgium. Association for Computational Linguistics. | es_ES |
dc.identifier.isbn | 978-1-948087-84-1 | |
dc.identifier.uri | http://hdl.handle.net/2183/35085 | |
dc.description | EMNLP 2018, Square Meeting Center, Brussels. From October 31st through November 4th. | es_ES |
dc.description.abstract | [Absctract]: We introduce a method to reduce constituent parsing to sequence labeling. For each word wt, it generates a label that encodes: (1) the number of ancestors in the tree that the words wt and wt+1 have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin. | 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/EC/H2020/714150 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/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.uri | https://doi.org/10.18653/v1/D18-1162 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Constituent Parsing | es_ES |
dc.subject | Penn Treebank | es_ES |
dc.subject | Sequence Labeling | es_ES |
dc.subject | Nonterminal Symbols | es_ES |
dc.title | Constituent 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.startPage | 1314 | es_ES |
UDC.endPage | 1324 | es_ES |
UDC.conferenceTitle | 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018) | es_ES |