@inproceedings{54c52137cb2b4d109797d3b7f134be3f,
title = "EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information",
abstract = "The identification of metaphor is a crucial prerequisite for many downstream language tasks, such as sentiment analysis, opinion mining, and textual entailment. State-of-the-art systems of metaphor detection implement heuristic principles such as Metaphor Identification Procedure (MIP) (Pragglejaz Group, 2007) and Selection Preference Violation (SPV) (Wilks, 1975; Wilson, 2002). We propose an innovative approach that leverages the cognitive information of embodiment that can be derived from word embeddings, and explicitly models the process of sensorimotor change that has been demonstrated as essential for human metaphor processing. We showed that this cognitively motivated module is effective and can improve metaphor detection, compared with the heuristic MIP that has been applied previously.",
author = "Yuxi Li and Bo Peng and Hsu, {Yu Yin} and Huang, {Chu Ren}",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 ; Conference date: 12-11-2024 Through 16-11-2024",
year = "2024",
month = nov,
doi = "10.18653/v1/2024.findings-emnlp.982",
language = "English",
series = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024",
publisher = "Association for Computational Linguistics (ACL)",
pages = "16868--16876",
editor = "Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen",
booktitle = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024",
address = "United States",
}