@inproceedings{li-etal-2024-embodiedbert,
title = "{E}mbodied{BERT}: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information",
author = "Li, Yu Xi and
Peng, Bo and
Hsu, Yu-Yin and
Huang, Chu-Ren",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.982/",
doi = "10.18653/v1/2024.findings-emnlp.982",
pages = "16868--16876",
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) and Selection Preference Violation (SPV). 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."
}
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<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) and Selection Preference Violation (SPV). 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.</abstract>
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%0 Conference Proceedings
%T EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information
%A Li, Yu Xi
%A Peng, Bo
%A Hsu, Yu-Yin
%A Huang, Chu-Ren
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-embodiedbert
%X 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) and Selection Preference Violation (SPV). 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.
%R 10.18653/v1/2024.findings-emnlp.982
%U https://aclanthology.org/2024.findings-emnlp.982/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.982
%P 16868-16876
Markdown (Informal)
[EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information](https://aclanthology.org/2024.findings-emnlp.982/) (Li et al., Findings 2024)
ACL