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dc.contributor.authorGritta, Milanen
dc.contributor.authorPilehvar, Mohammaden
dc.contributor.authorLimsopatham, Nen
dc.contributor.authorCollier, Nigelen
dc.date.accessioned2017-08-29T09:30:57Z
dc.date.available2017-08-29T09:30:57Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/266911
dc.description.abstractNamed entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve state-of-the-art results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.
dc.languageEngen
dc.titleVancouver Welcomes You! Minimalist Location Metonymy Resolutionen
dc.typeArticle
prism.publicationNameAssociation for Computational Linguisticsen
dc.identifier.doi10.17863/CAM.12975
dcterms.dateAccepted2017-03-31en
rioxxterms.versionofrecord10.18653/v1/P17-1115en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-03-31en
dc.contributor.orcidGritta, Milan [0000-0003-0014-7275]
dc.contributor.orcidCollier, Nigel [0000-0002-7230-4164]
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/M005089/1)
pubs.funder-project-idNERC (via Cranfield University) (NE/M009009/1)
pubs.funder-project-idMEDICAL RESEARCH COUNCIL (MR/M025160/1)
pubs.funder-project-idNERC (1649558)
cam.issuedOnline2017-07-28en


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