使用自然语言处理评估口头目击者的信心陈述
Rachel Leigh Greenspan1, Alex Lyman2, Paul Heaton2
1Department of Criminal Justice and Legal Studies, University of Mississippi.
Psychological science
|February 20, 2024
概括
研究人员开发了一个使用自然语言处理的自动化模型来分类口头目击者的信心. 这个模型准确地预测了目击者的准确性,为法律和科学应用提供了新的见解.
科学领域:
- 心理学 心理学 心理学
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
背景情况:
- 在法律调查中,目击者的信心至关重要.
- 传统上,在实验室中,信心是用数字来衡量的,但在现场,信心是用口头来衡量的.
- 目前分析口头信心的方法是有限的.
研究的目的:
- 开发一种自动化模型来分类口头目击者的信心陈述.
- 评估模型在预测目击者的信心水平 (高,中,低) 中的准确性.
- 引入一种新的度量,信心,用于测量陈述的模糊性.
主要方法:
- 使用自然语言处理方法.
- 培训并验证了来自4,541名成年证人的口头信任陈述的分类模型.
- 采用信心精度校准曲线来比较模型性能与自我报告的数值信心.
主要成果:
- 自动化模型在对目击者信心水平的分类中达到71%的准确性.
- 该模型的信心分类有效地预测了目击者的准确性,与自我报告的数字信心相比.
- 引入"信任"作为一种新的指标,提供关于目击者的准确性独立信息.
结论:
- 自动分析口头目击者信心是可行的和准确的.
- 该模型为实证科学家和执法部门在解释目击者的说法方面提供了一个有价值的工具.
- 信任提供了一个新的,客观的测量证人确定性及其与准确性的关系.
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