一种分析精神分裂症中语言紊乱的计算方法：整合惊奇度与连贯性度量
A Computational Approach to Analyzing Disrupted Language in Schizophrenia: Integrating Surprisal and Coherence Measures
AI总结：
本研究利用惊奇度和语义连贯性两种计算语言学度量，分析精神分裂症患者与健康对照组的语言差异，并揭示语言紊乱随症状严重程度的变化规律，为客观诊断提供依据。
摘要：
语言紊乱是精神分裂症症状的众所周知的影响之一。它们通常表现为言语混乱和话语连贯性受损。自发语言产生中的这些异常反映了潜在的认知障碍，并有可能作为精神分裂症症状严重程度和诊断的客观标志物。本研究关注如何用两种计算语言学度量来表征这些语言紊乱：惊奇度和语义连贯性。通过使用计算模型计算语言的惊奇度和语义连贯性，本研究考察了它们在精神分裂症患者和健康对照组之间的差异。此外，本研究进一步揭示了从这些语言度量的角度看，语言紊乱如何随精神分裂症症状严重程度的不同而变化。
英文摘要：
Language disruptions are one of the well-known effects of schizophrenia symptoms. They are often manifested as disorganized speech and impaired discourse coherence. These abnormalities in spontaneous language production reflect underlying cognitive disturbances and have the potential to serve as objective markers for symptom severity and diagnosis of schizophrenia. This study focuses on how these language disruptions can be characterized in terms of two computational linguistic measures: surprisal and semantic coherence. By computing surprisal and semantic coherence of language using computational models, this study investigates how they differ between subjects with schizophrenia and healthy controls. Furthermore, this study provides further insight into how language disruptions in terms of these linguistic measures change with varying degrees of schizophrenia symptom severity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
