对人格评估的深度学习方法:跨项目概括并扩大基于调查的研究范围
Suhaib Abdurahman1, Huy Vu2, Wanling Zou3
1Department of Psychology, University of Southern California.
Journal of personality and social psychology
|September 7, 2023
概括
这项研究使用自然语言处理和深度学习来从问卷单项中预测人格特征. 该方法准确地预测新项目评级,匹配人类表现,并提供一种可扩展的心理测量方法.
科学领域:
- 心理测量 心理测量
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 传统的人格评估调查仅限于先前调查的项目.
- 这限制了从调查中获得的信息量.
- 需要新的方法来进行更全面的心理测量.
研究的目的:
- 开发一种新的方法来预测参与者对新的,未经调查的人格调查问卷项的评分.
- 为心理评估利用统计自然语言处理方面的进步.
- 创建一个可扩展和具有成本效益的心理测量方法.
主要方法:
- 通过在大型英语数据上训练的深度神经网络,提取了问卷单项的"嵌入"表示.
- 构建了一个高维物体空间,在这个空间中,语言上相似的物体位于彼此附近.
- 结合项目嵌入与机器学习算法来推断参与者评级到新项目.
主要成果:
- 开发的方法准确地预测了新个性项的评分,达到与激励的人类评委相提并论的准确性.
- 该方法成功地识别了与仅基于语言内容的问卷单项相关的心理构造.
- 项目被准确地聚集到各自的构造中,仅使用语言信息.
结论:
- 来自深度语言模型的语言人格描述符的表示可以模拟和预测各种特征,尺度和构造.
- 这种方法为心理测量提供了一种新的,可扩展和具有成本效益的方法.
- 这些发现表明先进的NLP技术在推进人格评估方面的潜力.
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