DualCoTs：用于习语情感词典扩展的双链式思维提示
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
AI总结：
本文提出 DualCoTs 方法，利用大型语言模型和思维链提示，结合语言学与心理语言学见解，自动扩展中英文习语情感词典，并构建数据集 EmoIdiomE 验证了其有效性。
摘要：
习语是日常话语中传达情感的普遍载体，因此对习语情感的细致分析对于全面理解真实文本中的情感表达至关重要。然而，现有的习语情感分析语料库在很大程度上限制了文本情感分析的研究。在本文中，我们提出了一种创新方法，利用大型语言模型的能力，通过应用思维链提示来自动扩展习语的情感词典。为了证明该方法的有效性，我们整合了多种现有资源，构建了一个情感习语词典扩展数据集（称为 EmoIdiomE），其中包含了中英文习语的全面知识库。然后，我们设计了双链式思维方法，结合语言学和心理语言学的见解，以证明使用大模型自动扩展习语情感词典的有效性。实验表明，DualCoTs 在中英文的习语情感词典扩展中均有效。为保证可复现性，我们将在论文被接受后发布数据和代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
