Department of Neuropsychiatry, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
Faculty of Management and Law, Aomori Chuo Gakuin University, Aomori, Japan.
PLoS One. 2022 Feb 25;17(2):e0264204. doi: 10.1371/journal.pone.0264204. eCollection 2022.
The central symptom of autism spectrum disorder (ASD) is deficiency in social communication, which is generally viewed as being caused by pragmatic impairment (PI). PI is difficulty in using language appropriately in social situations. Studies have confirmed that PI is the result of neurological, cognitive, linguistic, and sensorimotor dysfunctions involving intricately intertwined factors. To elucidate the whole picture of this impairment, an approach from a multifaceted perspective fusing those factors is necessary. To this end, comprehensive PI mapping is a must, since no comprehensive mapping has yet been developed. The aim of this research is to present a model of annotation scheme development and corpus construction to efficiently visualize and quantify for statistical investigation occurrences of PI, which enables comprehensive mapping of PI in the spoken language of Japanese ASD individuals. We constructed system networks (lexicogrammatical option systems speakers make choices from) in the theoretical framework of Systemic Functional Linguistics, from which we developed an annotation scheme to comprehensively cover PI. Since system network covers all possible lexicogrammatical choices in linguistic interaction, it enables a comprehensive view of where and in what lexicogrammar PI occurs. Based on this annotation scheme, we successfully developed the Corpus of ASD + Typically Developed Spoken Language consisting of texts from 1,187 audiotaped tasks performed by 186 ASD and 106 typically developed subjects, accommodating approximately 1.07 million morphemes. Moreover, we were successful in the automatization of the annotation process by machine learning, accomplishing a 90 percent precision rate. We exemplified the mapping procedure with a focus on the spoken use of negotiating particles. Our model corpus is applicable to any language by incorporating our method of constructing the annotation scheme, and would give impetus to defining PI from a cross-linguistic point of view, which is needed because PI of ASD reflects cross-linguistic differences.
自闭症谱系障碍（ASD）的核心症状是社交沟通障碍，通常被认为是语用障碍（PI）所致。PI 是指在社交情境中使用语言不恰当的困难。研究已经证实，PI 是涉及错综复杂相互交织因素的神经、认知、语言和感觉运动功能障碍的结果。为了阐明这种障碍的全貌，需要从多方面融合这些因素的角度来进行研究。为此，必须进行全面的 PI 映射，因为目前还没有开发出全面的映射。本研究旨在提出一种标注方案开发和语料库构建模型，以有效地可视化和量化 PI 的发生情况，从而实现对日本 ASD 个体口语中 PI 的全面映射。我们在系统功能语言学的理论框架中构建了系统网络（说话者从中做出选择的词汇语法选项系统），并由此开发了一个标注方案，以全面涵盖 PI。由于系统网络涵盖了语言互动中所有可能的词汇语法选择，因此可以全面了解 PI 发生的位置和词汇语法。基于这个标注方案，我们成功地开发了由 186 名 ASD 和 106 名典型发育个体的 1187 个录音任务的文本组成的 ASD + 典型口语语料库，包含约 107 万个词素。此外，我们通过机器学习成功实现了标注过程的自动化，准确率达到 90%。我们通过重点关注协商粒子的口语使用来说明映射过程。我们的模型语料库可以通过纳入我们的标注方案构建方法应用于任何语言，并将有助于从跨语言的角度定义 PI，因为 ASD 的 PI 反映了跨语言的差异。
