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dc.contributor.authorNederhof, Mark Jan
dc.date.accessioned2019-06-04T10:30:03Z
dc.date.available2019-06-04T10:30:03Z
dc.date.issued2019-05-31
dc.identifier.citationNederhof , M J 2019 , ' Calculating the optimal step in shift-reduce dependency parsing : from cubic to linear time ' , Transactions of the Association for Computational Linguistics , vol. 7 , pp. 283-296 . https://doi.org/10.1162/tacl_a_00268en
dc.identifier.issn2307-387X
dc.identifier.otherPURE: 259065142
dc.identifier.otherPURE UUID: 309939cb-5253-4ef3-91c8-bda773cea8f2
dc.identifier.otherORCID: /0000-0002-1845-6829/work/58531291
dc.identifier.urihttp://hdl.handle.net/10023/17811
dc.description.abstractWe present a new cubic-time algorithm to calculate the optimal next step in shift-reduce dependency parsing, relative to ground truth, commonly referred to as dynamic oracle. Unlike existing algorithms, it is applicable if the training corpus contains non-projective structures. We then show that for a projective training corpus, the time complexity can be improved from cubic to linear.
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofTransactions of the Association for Computational Linguisticsen
dc.rights© 2019 Association for Computational Linguistics. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.en
dc.subjectFormal language theoryen
dc.subjectAutomata theoryen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputational Theory and Mathematicsen
dc.subjectT-NDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject~DC~en
dc.subject.lccQA75en
dc.titleCalculating the optimal step in shift-reduce dependency parsing : from cubic to linear timeen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1162/tacl_a_00268
dc.description.statusPeer revieweden


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