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A data-driven approach to the semantics of iconicity in American Sign Language and English

2020/03/01 by Bill Thompson, Marcus Perlman, Gary Lupyan +2 · 1 voice
Psychology · Computer Science · #Hearing Impairment and Communication #Language, Metaphor, and Cognition #Hand Gesture Recognition Systems

paper · pdf · doi:10.1017/langcog.2019.52

openalex publication_date 2020/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

abstract A growing body of research shows that both signed and spoken languages display regular patterns of iconicity in their vocabularies. We compared iconicity in the lexicons of American Sign Language (ASL) and English by combining previously collected ratings of ASL signs (Caselli, Sevcikova Sehyr, Cohen-Goldberg, & Emmorey, 2017) and English words (Winter, Perlman, Perry, & Lupyan, 2017) with the use of data-driven semantic vectors derived from English. Our analyses show that models of spoken language lexical semantics drawn from large text corpora can be useful for predicting the iconicity of signs as well as words. Compared to English, ASL has a greater number of regions of semantic space with concentrations of highly iconic vocabulary. There was an overall negative relationship between semantic density and the iconicity of both English words and ASL signs. This negative relationship disappeared for highly iconic signs, suggesting that iconic forms may be more easily discriminable in ASL than in English. Our findings contribute to an increasingly detailed picture of how iconicity is distributed across different languages.

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