用双思维链让大模型自动扩展成语情感词典
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
- 设计双思维链提示，融合语言学与心理语言学思路
- 在中英文成语情感词典扩展上效果显著
- 适合自然语言处理与情感分析研究者使用
成语是日常话语中表达情感的普遍载体，对其情感的细致分析对全面理解真实文本中的情绪表达至关重要。然而，现有成语情感分析语料库严重限制了文本情感研究的发展。本文提出一种创新方法，利用大语言模型的思维链提示技术，自动扩展成语情感词典。为验证该方法的有效性，整合多个现有资源，构建了情感成语词典扩展数据集（EmoIdiomE），涵盖中英文成语的丰富语料。设计了结合语言学与心理语言学洞察的双思维链（DualCoTs）方法，实验证明其在中英文成语情感词典扩展任务中均表现优异。为保证可复现性，论文将在接受后发布数据与代码。
原文摘要 · Abstract (English)
Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive understanding of emotional expression within real-world texts. Nevertheless, the existing corpora dedicated to idiom sentiment analysis considerably limit research in text sentiment analysis. In this paper, we propose an innovative approach to automatically expand the sentiment lexicon for idioms, leveraging the capabilities of large language models through the application of Chain-of-Thought prompting. To demonstrate the effectiveness of this approach, we integrate multiple existing resources and construct an emotional idiom lexicon expansion dataset (called EmoIdiomE), which encompasses a comprehensive repository of Chinese and English idioms. Then we designed the Dual Chain-of-Thoughts (DualCoTs) method, which combines insights from linguistics and psycholinguistics, to demonstrate the effectiveness of using large models to automatically expand the sentiment lexicon for idioms. Experiments show that DualCoTs is effective in idioms sentiment lexicon expansion in both Chinese and English. For reproducibility, we will release the data and code upon acceptance.
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