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Transition-based semantic role labeling with pointer networks
dc.contributor.author | Fernández-González, Daniel | |
dc.date.accessioned | 2023-03-24T12:22:27Z | |
dc.date.available | 2023-03-24T12:22:27Z | |
dc.date.issued | 2023-01-25 | |
dc.identifier.citation | Fernández-González, Daniel (2023) : Transition-based semantic role labeling with pointer networks. Knowledge-Based Systems, vol. 260, 110127 | es_ES |
dc.identifier.issn | 0950-7051 | |
dc.identifier.uri | http://hdl.handle.net/2183/32764 | |
dc.description.abstract | [Abstract] Semantic role labeling (SRL) focuses on recognizing the predicate–argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in , achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task. | es_ES |
dc.description.sponsorship | Ministerio de Ciencia e Innovación ; SCANNER-UDC,PID2020-113230RB-C21) | es_ES |
dc.description.sponsorship | CITIC ; ED431G 2019/01. | es_ES |
dc.description.sponsorship | Xunta de Galicia ; ED431C 2020/11 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/714150 | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.knosys.2022.110127 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Natural language processing | es_ES |
dc.subject | Computational linguistics | es_ES |
dc.subject | Semantic role labeling | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Deep learning | es_ES |
dc.title | Transition-based semantic role labeling with pointer networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Knowledge-Based Systems | es_ES |
UDC.volume | 260 | es_ES |
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