用计算语言学方法分析精神分裂症患者的语言混乱，揭示症状严重程度与语言异常的关系。
A Computational Approach to Analyzing Disrupted Language in Schizophrenia: Integrating Surprisal and Coherence Measures
- 结合意外度与语义连贯性模型分析患者语言
- 精神分裂症患者语言意外度更高、连贯性更差
- 该方法可量化症状严重程度，适合临床研究
语言障碍是精神分裂症的典型表现之一，常表现为言语紊乱和语篇连贯性受损。这些自发语言中的异常反映了潜在的认知功能障碍，有望成为症状严重程度和诊断的客观指标。本研究聚焦于两种计算语言学指标：意外度（surprisal）和语义连贯性（semantic coherence），通过计算模型分析精神分裂症患者与健康对照组的语言特征差异。结果表明，患者在语言生成中表现出更高的意外度和更低的语义连贯性。此外，研究还揭示了这些语言指标随症状严重程度变化的趋势，为理解语言障碍的动态演变提供了新视角。
原文摘要 · Abstract (English)
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.
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