SENSE-LM : A Synergy between a Language Model and Sensorimotor Representations for Auditory and Olfactory Information Extraction

Cédric Boscher, Christine Largeron, Véronique Eglin, Elöd Egyed-Zsigmond


Abstract
The five human senses – vision, taste, smell, hearing, and touch – are key concepts that shape human perception of the world. The extraction of sensory references (i.e., expressions that evoke the presence of a sensory experience) in textual corpus is a challenge of high interest, with many applications in various areas. In this paper, we propose SENSE-LM, an information extraction system tailored for the discovery of sensory references in large collections of textual documents. Based on the novel idea of combining the strength of large language models and linguistic resources such as sensorimotor norms, it addresses the task of sensory information extraction at a coarse-grained (sentence binary classification) and fine-grained (sensory term extraction) level.Our evaluation of SENSE-LM for two sensory functions, Olfaction and Audition, and comparison with state-of-the-art methods emphasize a significant leap forward in automating these complex tasks.
Anthology ID:
2024.findings-eacl.119
Volume:
Findings of the Association for Computational Linguistics: EACL 2024
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1695–1711
Language:
URL:
https://aclanthology.org/2024.findings-eacl.119
DOI:
Bibkey:
Cite (ACL):
Cédric Boscher, Christine Largeron, Véronique Eglin, and Elöd Egyed-Zsigmond. 2024. SENSE-LM : A Synergy between a Language Model and Sensorimotor Representations for Auditory and Olfactory Information Extraction. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1695–1711, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
SENSE-LM : A Synergy between a Language Model and Sensorimotor Representations for Auditory and Olfactory Information Extraction (Boscher et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-eacl.119.pdf
Video:
 https://aclanthology.org/2024.findings-eacl.119.mp4