IlliniMet: Illinois system for metaphor detection with contextual and linguistic information

Hongyu Gong, Kshitij Gupta, Akriti Jain, Suma Bhat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Metaphors are rhetorical use of words based on the conceptual mapping as opposed to their literal use. Metaphor detection, an important task in language understanding, aims to identify metaphors in word level from given sentences. We present IlliniMet, a system to automatically detect metaphorical words. Our model combines the strengths of the contextualized representation by the widely used RoBERTa model and the rich linguistic information from external resources such as WordNet. The proposed approach is shown to outperform strong baselines on a benchmark dataset. Our best model achieves F1 scores of 73.0% on VUA ALLPOS, 77.1% on VUA VERB, 70.3% on TOEFL ALLPOS and 71.9% on TOEFL VERB.

Original languageEnglish (US)
Title of host publicationACL 2020 - Figurative Language Processing, Proceedings of the 2nd Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages146-153
Number of pages8
ISBN (Electronic)9781952148125
DOIs
StatePublished - 2020
Event2nd Workshop on Figurative Language Processing 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: Jul 9 2020 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference2nd Workshop on Figurative Language Processing 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/9/20 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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