Show simple item record

dc.contributor.authorGómez-Rodríguez, Carlos
dc.contributor.authorVilares, David
dc.date.accessioned2024-01-23T13:52:57Z
dc.date.available2024-01-23T13:52:57Z
dc.date.issued2018
dc.identifier.citationCarlos 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.isbn978-1-948087-84-1
dc.identifier.urihttp://hdl.handle.net/2183/35085
dc.descriptionEMNLP 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.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/EC/H2020/714150es_ES
dc.relationinfo: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 PROFUNDOes_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.relation.urihttps://doi.org/10.18653/v1/D18-1162es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectConstituent Parsinges_ES
dc.subjectPenn Treebankes_ES
dc.subjectSequence Labelinges_ES
dc.subjectNonterminal Symbolses_ES
dc.titleConstituent 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.startPage1314es_ES
UDC.endPage1324es_ES
UDC.conferenceTitle2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)es_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record