ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure

arXiv:2309.03103 · cs.CL, cs.LG · Submitted 2023-09-06 · Read on arXiv

cs.CL, cs.LG

Submitted: 2023-09-06

Updated: 2024-03-23

Comments: 9 pages, 2 figures, accepted for the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Journal ref: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Turin, Italy, May, 2024

DOI: 10.63317/3xm2febvhgim

Code: https://github.com/melzohbi/ContrastWSD

License: http://creativecommons.org/licenses/by/4.0/

The gist: This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the

Terminology

Embeddings — 10× in this paper · explained in 2 episodes
Metaphor Identification Procedure — 9× in this paper
Word Sense Disambiguation — 8× in this paper
Benchmark — 5× in this paper · explained in 2 episodes
Transformer — 5× in this paper · explained in 2 episodes
Knowledge Graph — 4× in this paper · explained in 7 episodes
Alignment — 3× in this paper · explained in 1 episode
Data Augmentation — 3× in this paper · explained in 7 episodes
Metaphorical Polysemy Detection — 2× in this paper
Natural Language Processing — 2× in this paper
Clustering — 1× in this paper · explained in 2 episodes
Consistency — 1× in this paper · explained in 3 episodes
Cosine Similarity — 1× in this paper · explained in 3 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Graph Construction — 1× in this paper · explained in 3 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Semantic Gap — 1× in this paper · explained in 2 episodes

Abstract

This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.

Terminology

Abstract

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