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        <title>Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection</title>
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        <namePart type="given">Ghadi</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Metaphors use words from one domain of knowledge to describe another, which can make the meaning less clear and require human interpretation to understand. This makes it difficult for automated models to detect metaphorical usage. The objective of the experiments in the paper is to enhance the ability of deep learning models to detect metaphors automatically. This is achieved by using two elements of semantic richness, sensory experience, and body-object interaction, as the main lexical features, combined with the contextual information present in the metaphorical sentences. The tests were conducted using classification and sequence labeling models for metaphor detection on the three metaphorical corpora VUAMC, MOH-X, and TroFi. The sensory experience led to significant improvements in the classification and sequence labelling models across all datasets. The highest gains were seen on the VUAMC dataset: recall increased by 20.9%, F1 by 7.5% for the classification model, and Recall increased by 11.66% and F1 by 3.69% for the sequence labelling model. Body-object interaction also showed positive impact on the three datasets.</abstract>
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%0 Conference Proceedings
%T Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection
%A Alnafesah, Ghadi
%A Smith, Phillip
%A Lee, Mark
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F alnafesah-etal-2023-moved
%X Metaphors use words from one domain of knowledge to describe another, which can make the meaning less clear and require human interpretation to understand. This makes it difficult for automated models to detect metaphorical usage. The objective of the experiments in the paper is to enhance the ability of deep learning models to detect metaphors automatically. This is achieved by using two elements of semantic richness, sensory experience, and body-object interaction, as the main lexical features, combined with the contextual information present in the metaphorical sentences. The tests were conducted using classification and sequence labeling models for metaphor detection on the three metaphorical corpora VUAMC, MOH-X, and TroFi. The sensory experience led to significant improvements in the classification and sequence labelling models across all datasets. The highest gains were seen on the VUAMC dataset: recall increased by 20.9%, F1 by 7.5% for the classification model, and Recall increased by 11.66% and F1 by 3.69% for the sequence labelling model. Body-object interaction also showed positive impact on the three datasets.
%U https://aclanthology.org/2023.ranlp-1.9
%P 80-89

Markdown (Informal)

[Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection](https://aclanthology.org/2023.ranlp-1.9) (Alnafesah et al., RANLP 2023)

• Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection (Alnafesah et al., RANLP 2023)

ACL

• Ghadi Alnafesah, Phillip Smith, and Mark Lee. 2023. Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 80–89, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.