Year: 2023
Type: preprint
Abstract: We propose a metaphor detection architecture that is structured around two main modules: an expectation component that estimates representations of literal word expectations given a context, and a realization component that computes representations of actual word meanings in context. The overall architecture is trained to learn expectation-realization (ER) patterns that characterize metaphorical uses of words. When evaluated on three metaphor datasets for within distribution, out of distribution, and novel metaphor generalization, the proposed method is shown to obtain results that are competitive or better than state-of-the art. Further increases in metaphor detection accuracy are obtained through ensembling of ER models.
Source: arXiv (Cornell University)
Authors: Uduehi, Oseremen O., Bunescu, Razvan C.
Institutions: Ohio University, University of North Carolina at Charlotte
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388514756