项目在二进制语义分类计算模型中表现优于形容词
Evgeniia Diachek1, Sarah Brown-Schmidt1, Sean M Polyn2
1Department of Psychology and Human Development, Peabody College, Vanderbilt University.
Cognitive science
|September 11, 2023
概括
这项研究引入了一个语义记忆的计算模型,通过将它们与概念极端进行比较,成功分类了超过1500个单词. 这种方法模仿人类的语义分类,并预测响应时间.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 语义内存存储世界知识,分布式语义模型 (DSM) 在矢量空间中表示单词含义.
- 经典的DSM缺乏概念属性规范的机制,限制了它们在一般语义知识理论中的应用.
- 了解人类如何执行二进制语义分类任务对于完善语义记忆模型至关重要.
研究的目的:
- 为二进制语义分类任务 (例如,大小,动态) 开发一个计算模型.
- 评估各种DSM和分类机制的有效性.
- 提出一个与语义记忆的实例理论相一致的模型.
主要方法:
- 开发了一系列使用分布式语义模型的计算模型.
- 基于概念属性的二进制语义分类的实施机制.
- 在一个大数据集上评估模型超过1500个单词的分类准确性和响应时间预测.
主要成果:
- 最成功的模型创建了语义极端的复合表示 (例如",大"与"小").
- 这个模型在根据大小和动态性对单词进行分类时实现了人类范围的性能.
- 该模型准确地预测了分类任务中的人类响应时间.
结论:
- 人类可能会使用与任务相关的实例来表示语义极端,以做出分类决策.
- 拟议的模型为理解实例理论中的语义分类提供了一个计算框架.
- 这项工作通过结合概念财产表示和利用机制来推进DSM.
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