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阿尔巴:适应性基于语言的心理健康评估

Vasudha Varadarajan¹, Sverker Sikström², Oscar N E Kjell²

¹Department of Computer Science, Stony Brook University.

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting

|March 17, 2025

概括

基于语言的自适应性评估 (ALBA) 使用较少的问题来准确测量心理健康特征. 这种方法,特别是ALIRT,改善了有限的语言反应的心理特征评分.

科学领域:

心理测量心理测量心理测量
自然语言处理自然语言处理.
心理健康评估心理健康评估

背景情况:

心理健康状况呈现出各种症状,需要细致的评估.
目前基于语言的评估需要大量的数据来进行可靠的心理特征评分.
基于语言的自适应性评估 (ALBA) 旨在提高心理健康评估的效率和准确性.

研究的目的:

介绍和评估适应性基于语言的评估 (ALBA) 方法.
开发适应性测试策略,以使用有限的语言来评分潜在的心理特征.
比较包括古典测试理论和物品响应理论在内的心理测量方法.

主要方法:

开发了基于古典测试理论和物品响应理论的适应性测试方法.
引入了两种新的方法:一种基于物件响应理论的半监督方法 (ALIRT) 和一种监督的演员-批评模型.
对基于语言的自适应性评估进行实证评估的排序和评分策略.

主要成果:

无论是ALIRT还是Actor-Critic模型,在准确性和效率方面都超过了非自适应基线.
ALIRT展示了卓越的准确性和可扩展性,仅用3个问题实现了高相关性 (Pearson r ≈ 0.93).
基于语言的自适应性评估有效地利用了较小的抑郁和焦虑语言样本,保持有效性和较低的计算成本.

结论:

基于语言的自适应评估 (ALBA) 为心理特征测量提供了一种更有效的方法.
ALIRT为基于语言的心理健康评估提供了一个高度准确和可扩展的方法.
这种方法减少了对广泛语言数据的需求,使评估更可行.

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