新的嵌入式改进了风险感知的预测
Zak Hussain1,2, Rui Mata1, Dirk U Wulff1,2
1Faculty of Psychology, University of Basel, Missionsstrasse 60-62, Basel, 4055 Switzerland.
概括
新型语言嵌入模型,结合文本和自由关联,与预测风险感知的传统方法相匹配. 这种方法为分析真实世界的风险数据提供了更广泛的应用.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 经典的心理测量范式是评估风险感知的一种标准.
- 新的计算方法可能会在理解风险感知方面取得进展.
研究的目的:
- 评估语言嵌入是否可以改善或取代风险感知心理测量范式.
- 为风险感知研究引入巴塞尔风险规范数据集.
主要方法:
- 心理测量范式与文本和自由关联嵌入的比较.
- 开发一个整体模型,整合文本和自由关联嵌入.
- 使用巴塞尔风险规范数据集 (1004个风险来源) 进行验证.
主要成果:
- 整体嵌入模型实现了与心理测量范式相提并论的预测准确性.
- 这种新的方法捕获了风险感知 (影响,频率) 的额外维度.
- 整体模型证明了对现实世界文本数据的更广泛适用性,比如新闻标题.
结论:
- 语言嵌入,特别是文本和自由关联的集合,为传统方法提供了可行的替代方案.
- 这种方法为研究人员和政策制定者提供了一个强大的新工具,以监测公众的风险感知.
- 巴塞尔风险规范数据集促进了这一领域的进一步研究.
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