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dc.contributor.authorZhang, Yuan
dc.contributor.authorLi, Chengtao
dc.contributor.authorBarzilay, Regina
dc.contributor.authorDarwish, Kareem
dc.date.accessioned2017-07-17T18:38:17Z
dc.date.available2017-07-17T18:38:17Z
dc.date.issued2015-01
dc.identifier.urihttp://hdl.handle.net/1721.1/110740
dc.description.abstractIn this paper, we introduce a new approach for joint segmentation, POS tagging and dependency parsing. While joint modeling of these tasks addresses the issue of error propagation inherent in traditional pipeline archi-tectures, it also complicates the inference task. Past research has addressed this challenge by placing constraints on the scoring function. In contrast, we propose an approach that can handle arbitrarily complex scoring functions. Specifically, we employ a randomized greedy algorithm that jointly predicts segmentations, POS tags and dependency trees. Moreover, this architecture readily handles different seg-mentation tasks, such as morphological seg-mentation for Arabic and word segmentation for Chinese. The joint model outperforms the state-of-the-art systems on three datasets, obtaining 2.1% TedEval absolute gain against the best published results in the 2013 SPMRL shared task.en_US
dc.description.sponsorshipUnited States. Army Research Office (grant number W911NF-10-1-0533)en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agency. Broad Operational Language Translation (BOLT) Programen_US
dc.language.isoen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isversionofhttp://dblp.dagstuhl.de/db/conf/naacl/naacl2015.htmlen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT Web Domainen_US
dc.titleRandomized greedy inference for joint segmentation, POS tagging and dependency parsingen_US
dc.typeArticleen_US
dc.identifier.citationZhang, Yuan, Chengtao Li, Regina Barzilay and Kareem Darwish. "Randomized Greedy Inference for Joint Segmentation, POS Tagging and Dependency Parsing." Conference: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (January 2015. Denver, Colorado, May 31 - June 5, 2015) pp. 42-52.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorZhang, Yuan
dc.contributor.mitauthorLi, Chengtao
dc.contributor.mitauthorBarzilay, Regina
dc.relation.journalConference Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologiesen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsZhang, Yuan ; Li, Chengtao ; Barzilay, Regina ; Darwish, Kareemen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3121-0185
dc.identifier.orcidhttps://orcid.org/0000-0003-1532-3083
dc.identifier.orcidhttps://orcid.org/0000-0002-2921-8201
mit.licenseOPEN_ACCESS_POLICYen_US


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