A data-driven approach to the semantics of iconicity in American Sign Language and English. (March 2020)

• Record Type: Journal Article
• Authors: THOMPSON, BILL; PERLMAN, MARCUS; LUPYAN, GARY; SEVCIKOVA SEHYR, ZED; EMMOREY, KAREN
• Editors: Perlman, Marcus; Perniss, Pamela; Dingemanse, Mark
• Is Part Of: Language and cognition. Volume 12:Number 1(2020)
• Page Start: 182
• Page End: 202
• Publication Date: 2020-03
• Subjects: iconicity, -- computational semantics, -- American Sign Language, -- language modality, -- lexicon; Psycholinguistics -- Periodicals; 401.9
• DOI: 10.1017/langcog.2019.52
• Languages: English
• ISSNs: 1866-9808

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.