@inproceedings{gong-etal-2020-illinimet,
title = "{I}llini{M}et: {I}llinois System for Metaphor Detection with Contextual and Linguistic Information",
author = "Gong, Hongyu and
Gupta, Kshitij and
Jain, Akriti and
Bhat, Suma",
editor = "Klebanov, Beata Beigman and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee and
Feldman, Anna and
Ghosh, Debanjan",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.21",
doi = "10.18653/v1/2020.figlang-1.21",
pages = "146--153",
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.",
}
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%0 Conference Proceedings
%T IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information
%A Gong, Hongyu
%A Gupta, Kshitij
%A Jain, Akriti
%A Bhat, Suma
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gong-etal-2020-illinimet
%X 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.
%R 10.18653/v1/2020.figlang-1.21
%U https://aclanthology.org/2020.figlang-1.21
%U https://doi.org/10.18653/v1/2020.figlang-1.21
%P 146-153
Markdown (Informal)
[IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information](https://aclanthology.org/2020.figlang-1.21) (Gong et al., Fig-Lang 2020)
ACL