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dc.contributor.authorGómez-Rodríguez, Carlos
dc.contributor.authorRoca Rodríguez, Diego
dc.contributor.authorVilares, David
dc.date.accessioned2024-05-22T09:14:26Z
dc.date.available2024-05-22T09:14:26Z
dc.date.issued2023-12
dc.identifier.citationCarlos Gómez-Rodríguez, Diego Roca, and David Vilares. 2023. 4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6375–6384, Singapore. Association for Computational Linguistics.es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36571
dc.descriptionSingapore from Dec 6th to Dec 10th, 2023.es_ES
dc.description.abstract[Absctract]: We introduce an encoding for parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. The bits in each word’s label represent (1) whether it is a right or left dependent, (2) whether it is the outermost (left/right) dependent of its parent, (3) whether it has any left children and (4) whether it has any right children. We show that this provides an injective mapping from trees to labels that can be encoded and decoded in linear time. We then define a 7-bit extension that represents an extra plane of arcs, extending the coverage to almost full non-projectivity (over 99.9% empirical arc coverage). Results on a set of diverse treebanks show that our 7-bit encoding obtains substantial accuracy gains over the previously best-performing sequence labeling encodings.es_ES
dc.description.sponsorshipThis work has received funding by the European Research Council (ERC), under the Horizon Europe research and innovation programme (SALSA, grant agreement No 101100615), ERDF/MICINN-AEI (SCANNER-UDC, PID2020- 113230RB-C21), Xunta de Galicia (ED431C 2020/11), Grant GAP (PID2022-139308OA-I00) funded by MCIN/AEI/10.13039/501100011033/ and by ERDF “A way of making Europe”, and Centro de Investigación de Galicia “CITIC”, funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C2020/11es_ES
dc.language.isoenges_ES
dc.publisherAssociation for Computational Linguisticses_ES
dc.relationinfo:eu-repo/grantAgreement/EC/HE/101100615es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113230RB-C21/ES/MODELOS MULTITAREA DE ETIQUETADO SECUENCIAL PARA EL RECONOCIMIENTO DE ENTIDADES ENRIQUECIDO CON INFORMACIÓN LINGÜÍSTICA: SINTAXIS E INTEGRACIÓN MULTITAREA (SCANNER-UDC)es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-1393080A-100/ES/REPRESENTACIONES ESTRUCTURADAS VERDES Y ENCHUFABLESes_ES
dc.relation.urihttps://aclanthology.org/2023.emnlp-main.393/es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDependency parsinges_ES
dc.subjectSequence labelinges_ES
dc.subjectProjective and non-projective dependency treeses_ES
dc.title4 and 7-bit Labeling for Projective and Non-Projective Dependency Treeses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedings of the 2023 Conference on Empirical Methods in Natural Language Processinges_ES
UDC.startPage6375es_ES
UDC.endPage6384es_ES
UDC.conferenceTitle2023 Conference on Empirical Methods in Natural Language (EMNLP 2023)es_ES


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