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dc.contributor.authorRohanian, Omid
dc.contributor.authorTaslimipoor, Shiva
dc.contributor.authorEvans, Richard
dc.contributor.authorMitkov, Ruslan
dc.date.accessioned2018-10-09T15:14:55Z
dc.date.available2018-10-09T15:14:55Z
dc.date.issued2018-06-05
dc.identifier.isbn9781948087209
dc.identifier.urihttp://hdl.handle.net/2436/621771
dc.descriptionInternational Workshop on Semantic Evaluation. WLV at SemEval-2018 Task 3.
dc.description.abstractThis paper describes the systems submitted to SemEval 2018 Task 3 “Irony detection in English tweets” for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.
dc.description.sponsorshipResearch Group in Computational Linguistics
dc.formatapplication/PDF
dc.language.isoen
dc.publisherAssociation for Computational Linguistics
dc.subjectNatural Language Processing
dc.subjectIrony Detection
dc.subjectSentiment Analysis
dc.titleWLV at SemEval-2018 task 3: Dissecting tweets in search of irony
dc.typeConference contribution
pubs.place-of-publicationMassachusetts Institute of Technology (MIT), Cambridge, Massachusetts
dc.date.accepted2018-04
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUOW08102018OR
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2018-06-05
refterms.dateFCD2018-10-08T10:03:01Z
refterms.versionFCDAM
refterms.dateFOA2018-10-09T15:14:55Z


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