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Transition-based dependency parsing with stack long short-term memory

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dc.contributor.author Dyer, Chris
dc.contributor.author Ballesteros, Miguel
dc.contributor.author Ling, W
dc.contributor.author Matthews, A
dc.contributor.author Smith, Noah A.
dc.date.accessioned 2017-01-26T15:38:39Z
dc.date.available 2017-01-26T15:38:39Z
dc.date.issued 2015
dc.identifier.citation Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA. Transition-based dependency parsing with stack long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Volume 1: Long Papers; 2015 July 26-31; Beijing (China). [place unknown]: ACL; 2015. p. 334-43.
dc.identifier.uri http://hdl.handle.net/10230/28001
dc.description.abstract We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like the conventional stack data structures used in transitionbased parsing, elements can be pushed to/nor popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. This lets us formulate an efficient parsing model that captures three facets of a parser’s state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal/nstructures. Standard backpropagation techniques are used for training and yield state-of-the-art parsing performance.
dc.description.sponsorship This work was sponsored in part by the U. S. Army Research Laboratory and the U. S. Army Research Office/nunder contract/grant number W911NF-10-1-0533, and in part by NSF CAREER grant IIS-1054319./nMiguel Ballesteros is supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Volume 1: Long Papers; 2015 July 26-31; Beijing (China). [place unknown]: ACL; 2015. p. 334-43.
dc.rights © ACL, Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subject.other Lingüística computacional
dc.subject.other Tractament del llenguatge natural (Informàtica)
dc.title Transition-based dependency parsing with stack long short-term memory
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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