使用基于变压器的神经网络推断情感规范,并将其应用于实验性刺激选择
Hubert Plisiecki1, Adam Sobieszek2
1Institute of Psychology, Polish Academy of Sciences, SWPS University of Warsaw, Warsaw, Poland. hplisiecki@gmail.com.
Behavior research methods
|September 25, 2023
概括
这项研究引入了一种新的神经网络模型,用于预测多个维度和语言的单词情感性. 该模型提高了情绪分析和实验性刺激选择的准确性,提供了广泛的研究应用.
科学领域:
- 计算语言学计算语言学
- 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:
- 情感性规范 (价值,兴奋) 对于NLP任务如情感分析和实验设计至关重要.
- 目前对单词情感性的数据集有限,特别是对于非英语语言和较新的情感维度.
- 以前用于推断情绪规范的方法在准确性和范围方面存在局限性.
研究的目的:
- 开发一种基于变压器的新型神经网络,用于同时推断多个语义和情感词语规范.
- 与现有的方法相比,提高预测人类对词语情感性判断的准确性.
- 为研究人员提供一种多功能工具,通过在各种语言中进行规范推断,并提供实际应用.
主要方法:
- 提出了一个基于变压器的神经网络架构,用于共同预测各种单词规范.
- 通过将预测规范与人类判断进行比较来评估模型性能,实现最先进的相关性.
- 开发了一种非监督刺激选择的方法,该方法基于使用外推模型对语义内容进行匹配.
主要成果:
- 拟议的模型实现了与人类判断的最先进的相关性,平均精度提高了Δr = 0.1.1.
- 证明了模型能够同时推断出广泛的语义和情感规范的能力.
- 成功地应用该模型来生成英语,波兰语,荷兰语,德语,法语和西班牙语的规范外推.
结论:
- 发达的神经网络在预测单词情感性和语义规范方面取得了重大进展.
- 该模型的跨语言适用性和改进的准确性解决了心理语言学和NLP中的关键数据缺口.
- 可访问的网络应用程序和刺激选择方法促进了更广泛的研究使用和发现.
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