Abstract
Metaphor 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.Citation
Rohanian, 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.Publisher
ACLAdditional Links
https://www.aclweb.org/anthology/2020.acl-main.259/Type
Conference contributionLanguage
enDescription
© 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.259ISBN
9781952148255ae974a485f413a2113503eed53cd6c53
10.18653/v1/2020.acl-main.259
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/