@InProceedings{prudhommeaux-vansanten-gliner:2017:Short,
  author    = {Prud'hommeaux, Emily  and  van Santen, Jan  and  Gliner, Douglas},
  title     = {Vector space models for evaluating semantic fluency in autism},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {32--37},
  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.
	\end{abstract}},
  url       = {http://aclweb.org/anthology/P17-2006}
}