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dc.contributor.authorHough, Jen_US
dc.contributor.authorPurver, Men_US
dc.date.accessioned2016-02-10T15:22:28Z
dc.date.issued2014-10en_US
dc.identifier.isbn978-1-937284-96-1en_US
dc.identifier.urihttps://aclweb.org/anthology/D/D14/D14-1000.pdf.
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/11090
dc.description.sponsorshipHough is supported by the DUEL project, financially supported by the Agence Nationale de la Research (grant number ANR-13-FRAL-0001) and the Deutsche Forschungsgemainschaft. Much of the work was carried out with support from an EPSRC DTA scholarship at Queen Mary University of London. Purver is partly supported by ConCreTe: the project ConCreTe acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733en_US
dc.format.extent78 - 89en_US
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.titleStrongly Incremental Repair Detectionen_US
dc.typeConference Proceeding
dc.rights.holder2014 Association for Computational Linguistics
pubs.author-urlhttp://arxiv.org/abs/1408.6788en_US
pubs.notesNo embargoen_US
pubs.publisher-urlhttp://www.aclweb.org/anthology/D14-1009en_US
qmul.funderRobust Incremental Semantic Resources for Dialogue::Engineering and Physical Sciences Research Councilen_US
qmul.funderRobust Incremental Semantic Resources for Dialogue::Engineering and Physical Sciences Research Councilen_US


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