Computational characterization of language use in autism spectrum disorder

描述（由申请人提供）：非典型或受损语言是自闭症谱系障碍（ASD）的核心特征之一。然而，ASD中语言的确切特征是什么，以及它们与其他疾病（如特殊语言障碍（SLI））有何不同，在很大程度上仍然是未知的。研究语言障碍的一个重要障碍是，传统的结构化工具（即由一系列项目组成的工具，每个项目通常引起简短的反应，如临床语言基础评估[CELF]）可能无法提供足够的信息广度：拟议的研究将建立在“自然语言处理”（NLP）技术的最新进展上，这是计算机科学的一个领域，涉及文本的计算分析。该研究的目标是开发和验证基于自然语言样本的原始（即未编码）转录本自动测量ASD语言特征的新的基于NLP的方法。目标是通过提高提取信息的效率、可靠性和丰富性来改进自然语言样本的分析。将分析从早期研究中获得的三组4至8岁儿童的数据：ASD、特殊语言障碍和典型发育儿童。如果成功，新方法将对ASD和其他语言受影响的疾病的研究和临床实践产生重要影响，通过分析更具代表性和生态有效的自然语言样本，以及通过轻松提取大量语言特征，为发现目前未知的ASD语言特征创造机会。

DESCRIPTION (provided by applicant): Atypical or impaired language is one of the core features of autism spectrum disorder (ASD). Yet, what the precise characteristics of language are in ASD and how they differ from those in other disorders such as Specific Language Impairment (SLI) is still substantially unknown. An important obstacle for the study of language in any disorder is that conventional structured instruments (i.e., instruments consisting of a sequence of items, each eliciting a - typically brief - response, such as the Clinical Evaluation of Language Fundamentals [CELF]) may not provide adequate breadth of information: Analysis of natural language samples is required The proposed research will build on recent progress in "Natural Language Processing" (NLP) technology, an area of Computer Science concerned with computational analysis of text. The goal of the proposed research is to develop and validate new NLP based methods that automatically measure language characteristics of ASD based on raw (i.e., not coded) transcripts of natural language samples. The objective is to improve the analysis of natural language samples by enhancing efficiency, reliability, and richness of information extracted. Data on three groups of children ages four to eight will be analyzed, obtained from an earlier study: ASD, SLI, and typically developing children. If successful, the new methods will have important impacts on research and clinical practice for ASD and for other disorders in which language is affected, by enabling analysis of more representative and ecologically valid natural language samples as well as by creating opportunities for discovery of currently unknown language characteristics of ASD by the effortless extraction of numerous language features.