Metaphor Detection via Explicit Basic Meanings Modelling
Yucheng Li, Shun Wang, Chenghua Lin, Guerin Frank

TL;DR
This paper introduces a novel metaphor detection approach that models basic word meanings based on literal annotations, outperforming existing methods and reaching near the theoretical upper bound on benchmark datasets.
Contribution
It proposes explicitly modeling basic meanings from literal annotations, aligning with linguistic theories, and demonstrates significant performance improvements.
Findings
Outperforms state-of-the-art by 1.0% in F1 score
Reaches the theoretical upper bound on VUA18 benchmark
Highlights the importance of modeling basic meanings
Abstract
One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearly defines that the metaphoricity of a lexical unit is determined based on the contrast between its \textit{contextual meaning} and its \textit{basic meaning}, existing work does not strictly follow this principle, typically using the \textit{aggregated meaning} to approximate the basic meaning of target words. In this paper, we propose a novel metaphor detection method, which models the basic meaning of the word based on literal annotation from the training set, and then compares this with the contextual meaning in a target sentence to identify metaphors. Empirical results show that our method outperforms the state-of-the-art method significantly by 1.0\% in F1 score. Moreover, our performance even reaches the…
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Taxonomy
TopicsLanguage, Metaphor, and Cognition · Natural Language Processing Techniques · English Language Learning and Teaching
