Computational characterization of language use in autism spectrum disorder

描述(申请人提供)：语言不典型或受损是自闭症谱系障碍(ASD)的核心特征之一。然而，ASD患者语言的确切特征是什么，以及它们与其他疾病(如特定语言障碍(SLI))的不同之处仍是未知的。对任何障碍的语言进行研究的一个重要障碍是，传统的结构化工具(即由一系列项目组成的工具，每个项目都引起-通常简短的反应，如语言基础的临床评估[CELF])可能不能提供足够广泛的信息：需要对自然语言样本进行分析。拟议的研究将建立在“自然语言处理”(NLP)技术的最新进展的基础上，这是计算机科学中关注文本计算分析的一个领域。这项研究的目标是开发和验证基于自然语言处理的新方法，这些方法基于自然语言样本的原始(即未编码)转录，自动测量ASD的语言特征。其目的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：自闭症、系统性红斑狼疮和典型的发育中儿童。如果成功，新方法将通过分析更具代表性和生态上有效的自然语言样本，并通过毫不费力地提取大量语言特征，为发现目前未知的ASD语言特征创造机会，从而对ASD和其他语言障碍的研究和临床实践产生重要影响。

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.