招募大小表示来理解分级词语
Sashank Varma1, Emily M Sanford2, Vijay Marupudi3
1School of Interactive Computing, Georgia Institute of Technology, United States; School of Psychology, Georgia Institute of Technology, United States.
Cognitive psychology
|August 2, 2024
概括
用于数字理解的幅度表示也适用于分级词,挑战纯统计语言模型. 这表明共享的认知资源用于语言和数字理解.
科学领域:
- 认知心理学 认知心理学
- 语言学的语言学.
- 神经科学是一个神经科学.
背景情况:
- 之前的研究主要探讨了语言如何影响数学思维.
- 目前的研究调查了相反的情况:数值理解如何影响语言处理.
研究的目的:
- 确定数值感至关重要的大小表示是否也用于理解分级单词.
- 探索数值和语言大小处理之间的关系.
主要方法:
- 实验1:比较范式以确定分级词的距离,比率和边界效应.
- 实验2:相似度评分和多维缩放,以逐级理解单词.
- 实验3:机器学习模型 (LSA,word2vec,GloVe等等) 的使用 与人类数据进行了测试.
主要成果:
- 有证据表明,大小表示效应 (距离,比率,边界) 从数字延伸到分级词.
- 人类数据显示了这些效应,但领先的机器学习模型没有完全复制它们.
- 仅基于分布式语义的机器学习模型未能捕捉到人类分级词理解的完整模式.
结论:
- 大小表示在理解分级词语方面发挥着作用,表明了与数值认知的潜在重叠.
- 超越统计语言模式的认知过程是理解分级词语含义所必需的.
- 这项研究为在更广泛的语言理解中研究大小表示以及语言和数字理解的共享/独立性质开辟了道路.
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