title: Distributional semantic models for the evaluation of disordered language.
abstract: Atypical semantic and pragmatic expression is frequently reported in the language of children with autism. Although this atypicality often manifests itself in the use of unusual or unexpected words and phrases, the rate of use of such unexpected words is rarely directly measured or quantified. In this paper, we use distributional semantic models to automatically identify unexpected words in narrative retellings by children with autism. The classification of unexpected words is sufficiently accurate to distinguish the retellings of children with autism from those with typical development. These techniques demonstrate the potential of applying automated language analysis techniques to clinically elicited language data for diagnostic purposes.
authors.affiliation: Center for Spoken Language Understanding, Oregon Health & Science University.
authors.forename: Masoud
authors.initials: M
authors.lastname: Rouhizadeh
authors.affiliation: Center for Language Sciences, University of Rochester.
authors.forename: Emily
authors.initials: E
authors.lastname: Prud'hommeaux
authors.forename: Brian
authors.initials: B
authors.lastname: Roark
authors.forename: Jan
authors.initials: J
authors.lastname: van Santen