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Autori principali: Uduehi, Oseremen O., Bunescu, Razvan C.
Natura: Preprint
Pubblicazione: 2023
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Accesso online:https://arxiv.org/abs/2311.03963
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author Uduehi, Oseremen O.
Bunescu, Razvan C.
author_facet Uduehi, Oseremen O.
Bunescu, Razvan C.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2311_03963
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle An Expectation-Realization Model for Metaphor Detection
Uduehi, Oseremen O.
Bunescu, Razvan C.
Computation and Language
Artificial Intelligence
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.
title An Expectation-Realization Model for Metaphor Detection
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2311.03963