Paper

Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics

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Abstract

Many of the most significant impairments faced by individuals with autism spectrum disorder (ASD) relate to pragmatic (i.e. social) language. There is also evidence that pragmatic language differences may map to ASD-related genes. Therefore, quantifying the social-linguistic features of ASD has the potential to both improve clinical treatment and help identify gene-behavior relationships in ASD. Here, we apply vector semantics to transcripts of semi-structured interactions with children with both idiopathic and syndromic ASD. We find that children with ASD are less semantically similar to a gold standard derived from typically developing participants, and are more semantically variable. We show that this semantic similarity measure is affected by transcript word length, but that these group differences persist after removing length differences via subsampling. These findings suggest that linguistic signatures of ASD pervade child speech broadly, and can be automatically detected even in less structured interactions.

Keywords: autism spectrum disorder, word embeddings, semantic distance

How to Cite:

Goodkind, A., Lee, M., Martin, G. E., Losh, M. & Bicknell, K., (2018) “Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics”, Society for Computation in Linguistics 1(1), 12-22. doi: https://doi.org/10.7275/R56W988P