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dc.contributor.authorRohanian, Omid
dc.contributor.authorRei, Marek
dc.contributor.authorTaslimipoor, Shiva
dc.contributor.authorHa, Le
dc.date.accessioned2020-06-04T11:04:12Z
dc.date.available2020-06-04T11:04:12Z
dc.date.issued2020-07-06
dc.identifier.citationRohanian, O., Rei, M., Taslimipoor, S. and Ha, L.A. (2020) Verbal multiword expressions for identification of metaphor, Proceedings of the 58th annual meeting of the Association for Computational Linguistics (ACL), 6th-8th July, 2020, pp. 2890–2895.en
dc.identifier.isbn9781952148255
dc.identifier.doi10.18653/v1/2020.acl-main.259
dc.identifier.urihttp://hdl.handle.net/2436/623243
dc.description© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.18653/v1/2020.acl-main.259en
dc.description.abstractMetaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first “MWE-aware” metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherACLen
dc.relation.urlhttps://www.aclweb.org/anthology/2020.acl-main.259/en
dc.subjectmultiword expressionsen
dc.subjectMetaphoren
dc.subjectDeep Learningen
dc.titleVerbal multiword expressions for identification of metaphoren
dc.typeConference contributionen
dc.date.updated2020-06-02T11:52:34Z
dc.conference.nameThe 58th Annual Meeting of the Association for Computational Linguistics
dc.conference.nameThe 58th Annual Meeting of the Association for Computational Linguistics
dc.conference.locationOnline
pubs.finish-date2020-07-08
pubs.start-date2020-07-06
dc.date.accepted2020-04-23
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW04062020LHen
rioxxterms.versionVORen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2020-07-06en
refterms.dateFCD2020-06-04T10:58:05Z
refterms.versionFCDVOR
refterms.dateFOA2020-07-06T00:00:00Z


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