Metaphor Detection via Explicit Basic Meanings Modelling
This addresses metaphor detection in NLP, an incremental improvement with specific gains for linguistic analysis tasks.
The paper tackles metaphor detection by modeling a word's basic meaning from literal annotations and comparing it to contextual meaning, outperforming the state-of-the-art by 1.0% in F1 score and reaching the theoretical upper bound on the VUA18 benchmark for certain targets.
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 theoretical upper bound on the VUA18 benchmark for targets with basic annotations, which demonstrates the importance of modelling basic meanings for metaphor detection.