Vector space models for evaluating semantic fluency in autism

Emily Prud’hommeaux, Jan van Santen, Douglas Gliner


Abstract
A common test administered during neurological examination is the semantic fluency test, in which the patient must list as many examples of a given semantic category as possible under timed conditions. Poor performance is associated with neurological conditions characterized by impairments in executive function, such as dementia, schizophrenia, and autism spectrum disorder (ASD). Methods for analyzing semantic fluency responses at the level of detail necessary to uncover these differences have typically relied on subjective manual annotation. In this paper, we explore automated approaches for scoring semantic fluency responses that leverage ontological resources and distributional semantic models to characterize the semantic fluency responses produced by young children with and without ASD. Using these methods, we find significant differences in the semantic fluency responses of children with ASD, demonstrating the utility of using objective methods for clinical language analysis.
Anthology ID:
P17-2006
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–37
Language:
URL:
https://aclanthology.org/P17-2006
DOI:
10.18653/v1/P17-2006
Bibkey:
Cite (ACL):
Emily Prud’hommeaux, Jan van Santen, and Douglas Gliner. 2017. Vector space models for evaluating semantic fluency in autism. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 32–37, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Vector space models for evaluating semantic fluency in autism (Prud’hommeaux et al., ACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/P17-2006.pdf