一个大规模的数据库的普通话中文词汇协会从词汇的小世界项目项目
Bing Li1,2, Ziyi Ding1, Simon De Deyne3
1Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Behavior research methods
|December 31, 2024
概括
词汇-中文的小世界 (SWOW-ZH) 数据集提供了关键的普通话中文词汇关联数据. 该资源增强了对语义网络的理解,并在预测人类词汇关系方面优于人工智能模型.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理语言学 心理语言学
背景情况:
- 词汇关联是理解单词含义和语义关系的关键.
- 现有的大规模数据集是有限的,特别是在普通话中文.
- 需要全面的中文词汇关联数据.
研究的目的:
- 介绍词语的小世界 - 钟文 (SWOW-ZH) 数据集.
- 为普通话中文建立一个语义网络.
- 评估词汇关联措施的预测能力.
主要方法:
- 从4万多名参与者中收集了超过1万个中文提示词的三回应词汇协会.
- 从SWOW-ZH数据构建了一个语义网络.
- 通过预测人类处理延迟并与基于文本和嵌入措施进行比较来评估并发有效性.
主要成果:
- 在SWOW-ZH网络中的单词中心性显著预测了词汇决策和单词命名速度.
- 在预测人类词汇关系方面,SWOW-ZH数据优于基于文本的嵌入式和大型语言模型.
- 确定了中文单词联想的独特特征,包括单词形成模式.
结论:
- 大规模的人体实验数据对于理解语言复杂性至关重要.
- SWOW-ZH数据集为中国语言研究提供了宝贵的资源.
- 人类衍生词汇关联数据提供了超越计算模型的独特见解.
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