Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/93757
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGoodman, Michael Wayneen
dc.contributor.authorBond, Francisen
dc.date.accessioned2009-11-13T06:51:47Zen
dc.date.accessioned2019-12-06T18:44:58Z-
dc.date.available2009-11-13T06:51:47Zen
dc.date.available2019-12-06T18:44:58Z-
dc.date.copyright2009en
dc.date.issued2009en
dc.identifier.citationGoodman, M. W., & Bond, F. (2009). Using generation for grammar analysis and error detection. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers (2009:Singapore): pp. 109–112.en
dc.identifier.urihttps://hdl.handle.net/10356/93757-
dc.description.abstractWe demonstrate that the bidirectionality of deep grammars, allowing them to generate as well as parse sentences, can be used to automatically and effectively identify errors in the grammars. The system is tested on two implemented HPSG grammars: Jacy for Japanese, and the ERG for English. Using this system, we were able to increase generation coverage in Jacy by 18% (45% to 63%) with only four weeks of grammar development.en
dc.format.extent5 p.en
dc.language.isoenen
dc.rightsProceedings of the ACL-IJCNLP 2009 Conference Short Papers (2009:Singapore) © copyright 2009 ACL and AFNLP. The conference paper is available at www.acl-ijcnlp-2009.org/en
dc.subjectDRNTU::Humanities::Linguisticsen
dc.titleUsing generation for grammar analysis and error detectionen
dc.typeConference Paperen
dc.contributor.schoolSchool of Humanities and Social Sciencesen
dc.contributor.conferenceAnnual Meeting of the Association for Computational Linguistics (47th : 2009 : Singapore)en
dc.identifier.openurlhttp://www.acl-ijcnlp-2009.org/main/acceptedshortpapers.htmlen
dc.description.versionAccepted versionen
dc.identifier.rims148255en
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:HSS Conference Papers
Files in This Item:
File Description SizeFormat 
148255_1.pdfAccepted version80.47 kBAdobe PDFThumbnail
View/Open

Page view(s) 5

835
Updated on Apr 11, 2021

Download(s) 5

454
Updated on Apr 11, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.