arXiv 2604.278462026-09-10cs.CL

多层级叙事评估在心理健康预测中优于词法特征

Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health

Institute for Artificial Intelligence（人工智能研究院）；School of Psychological and Cognitive Sciences（心理学与认知科学学院）；School of Intelligence Science and Technology（智能科学与技术学院）；State Key Laboratory of General AI（通用人工智能国家重点实验室）；Beijing Key Laboratory of Behavior and Mental Health（北京行为与心理健康重点实验室）；PKU-Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）

Yuxi Ma, Jieming Cui, Muyang Li, Ye Zhao, Yu Li, Yixuan Wang, Chi Zhang, Yinyin Zang, Yixin Zhu

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AI总结：

本文提出多层级框架，通过830篇中文治疗文本验证，宏观层面的LLM叙事评估在心理健康预测中表现最佳，挑战了词频统计为主的传统方法。

摘要：

人们叙述经历的方式揭示了心智如何组织这些经历。计算方法从词频统计发展到神经方法，但仍碎片化：词典工具遗漏话语结构，嵌入式方法混淆局部连贯性与全局组织。本文引入三级框架——微观层面的词法特征、中观层面的语义嵌入和宏观层面的LLM叙事评估，并在830篇中文治疗文本（涵盖抑郁、焦虑和创伤）上证明，宏观层面评估在心理健康预测中显著优于词法和嵌入特征。这挑战了领域对词频统计的重视：形式结构特征（Labov的故事语法、RST连贯性、命题组成）表明，叙述组织本身具有预测信号，而临床导向的叙述维度捕捉了心理状态通过话语表达的方式。语义嵌入添加了最小的独立价值，但在多层级分类中产生增量收益。通过将计算层级扎根于话语处理理论，该框架将宏观结构组织视为临床信号的主要来源，并为干预设计和纵向研究生成可检验的假设。

英文摘要：

How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved from lexical counting to neural methods, yet remain fragmented: dictionary tools miss discourse structure, while embeddings conflate local coherence with global organization. No existing framework maps these techniques onto the hierarchical processes through which narratives are constructed. Here we introduce a three-level framework - micro-level lexical features, meso-level semantic embeddings, and macro-level LLM narrative evaluation - and show, across 830 Chinese therapeutic texts spanning depression, anxiety, and trauma, that macro-level evaluation substantially outperforms lexical and embedding features for mental health prediction. This challenges the field's emphasis on word-counting: formal structural features (Labov's story grammar, RST coherence, propositional composition) demonstrate that narrative organization per se carries predictive signal, while clinically-grounded narrative dimensions capture how psychological states are expressed through discourse. Semantic embeddings add minimal independent value but yield incremental gains in multi-level classification. By grounding computational levels in discourse processing theory, this framework identifies macro-structural organization as the primary locus of clinical signal and generates testable hypotheses for intervention design and longitudinal research.

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。