Prud'hommeaux Emily, Rouhizadeh Masoud
Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon, USA.
Workshop Child Comput Interact. 2012 Sep 14;2012:1-6.
Autism spectrum disorder (ASD) is characterized by atypical and idiosyncratic language, which often has its roots in pragmatic deficits. Identifying and measuring pragmatic language ability is challenging and requires substantial clinical expertise. In this paper, we present a method for automatically identifying pragmatically inappropriate language in narratives using two features related to relevance and topicality. These features, which are derived using techniques from machine translation and information retrieval, are able to distinguish the narratives from children with ASD from those of their language-matched peers and may prove useful in the development of automated screening tools for autism and neurodevelopmental disorders.
自闭症谱系障碍（ASD）的特征是语言不典型且独特，其根源往往在于语用缺陷。识别和测量语用语言能力具有挑战性，需要大量临床专业知识。在本文中，我们提出了一种方法，利用与相关性和主题性相关的两个特征自动识别叙事中语用不恰当的语言。这些特征是使用机器翻译和信息检索技术得出的，能够区分自闭症谱系障碍儿童的叙事与语言匹配的同龄人，并可能在自闭症和神经发育障碍的自动筛查工具开发中发挥作用。
