实现语义属性规范的可扩展和可靠编码:ChatGPT与改进的AC-PLT相比
Diego Ramos1, Sebastián Moreno2, Enrique Canessa2
1Faculty of Engineering and Science, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolen, Santiago, Chile. dramos@alumnos.uai.cl.
Behavior research methods
|October 6, 2025
概括
本研究优化了物业列表任务的辅助编码 (AC-PLT) 用于语义内容分析. 增强的AC-PLT框架与E5嵌入和kNN分类实现了比以前的方法更高的准确性和稳定性.
科学领域:
- 认知心理学 认知心理学
- 心理语言学 心理语言学
- 自然语言处理自然语言处理.
背景情况:
- 使用概念财产规范 (CPN) 的属性列表任务 (PLT) 对语义内容的手动编码带来了重大挑战.
- 现有的框架需要优化以提高效率并减少编码过程中的变化.
研究的目的:
- 加强物业上市任务辅助编码 (AC-PLT) 框架,以实现更准确和更有效的语义内容编码.
- 通过评估各种文本清理,嵌入模型和分类方法来优化AC-PLT.
主要方法:
- 为了评估文本清理技术的组合,嵌入模型 (Word2Vec,E5,LaBSE) 和分类器 (kNN,SVM,XGBoost) 的结合,进行了废除研究.
- 使用CPN27和CPN120数据集评估性能,并将结果与原始AC-PLT基线和ChatGPT进行比较.
主要成果:
- 结合E5嵌入模型和kNN分类,获得了最高的精度,达到0.523 (CPN27) 和0.608 (CPN120) 的顶级测试精度.
- 与ChatGPT相比,优化的AC-PLT显示出更高的稳定性和成本效益,特别是在大规模应用中.
结论:
- 改进的AC-PLT框架为语义内容分析中的手动编码挑战提供了可扩展和高效的解决方案.
- 未来的研究将专注于将AC-PLT集成为推系统,以进一步协助人类编码者进行认知和心理语言研究.
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