Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations

摘要

While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present \mathit{SENSE} (\textbf{S}ensorimotor \textbf{E}mbedding \textbf{N}orm \textbf{S}coring \textbf{E}ngine), 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 \mathit{SENSE} 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.