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        <title>Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations</title>
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    <abstract>While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present \mathitSENSE (Sensorimotor Embedding Norm Scoring Engine), a learned projection model that predicts Lancaster sensorimotor norms from word lexical embeddings. We also conducted a behavioral study where 281 participants selected which among candidate nonce words evoked specific sensorimotor associations, finding statistically significant correlations between human selection rates and \mathitSENSE ratings across 6 of the 11 modalities. Sublexical analysis of these nonce word selection rates revealed systematic phonesthemic patterns for the interoceptive norm, suggesting a path towards computationally proposing candidate phonesthemes from text data.</abstract>
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%0 Conference Proceedings
%T Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations
%A Gupta, Abhinav
%A Mintz, Toben
%A Thomason, Jesse
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F gupta-etal-2026-words
%X While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present \mathitSENSE (Sensorimotor Embedding Norm Scoring Engine), a learned projection model that predicts Lancaster sensorimotor norms from word lexical embeddings. We also conducted a behavioral study where 281 participants selected which among candidate nonce words evoked specific sensorimotor associations, finding statistically significant correlations between human selection rates and \mathitSENSE ratings across 6 of the 11 modalities. Sublexical analysis of these nonce word selection rates revealed systematic phonesthemic patterns for the interoceptive norm, suggesting a path towards computationally proposing candidate phonesthemes from text data.
%R 10.18653/v1/2026.findings-acl.2038
%U https://aclanthology.org/2026.findings-acl.2038/
%U https://doi.org/10.18653/v1/2026.findings-acl.2038
%P 41021-41029

Markdown (Informal)

[Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations](https://aclanthology.org/2026.findings-acl.2038/) (Gupta et al., Findings 2026)

• Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations (Gupta et al., Findings 2026)

ACL

• Abhinav Gupta, Toben Mintz, and Jesse Thomason. 2026. Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41021–41029, San Diego, California, United States. Association for Computational Linguistics.