历史墨迹：探索大型语言模型在19世纪西班牙语中的反讽检测
Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish
AI总结：
本研究利用大型语言模型（BERT和GPT-4o）增强19世纪拉丁美洲报纸的反讽检测，通过半自动注释方法解决类别不平衡，贡献了新历史西班牙语数据集和融合历史文化的注释方法论。
摘要：
本研究探讨了使用大型语言模型（LLMs）来增强数据集并改进19世纪拉丁美洲报纸中的反讽检测。我们采用了两种策略来评估BERT和GPT-4o模型在捕捉反讽微妙细微差别方面的有效性，通过多类别和二元分类任务进行。首先，我们实施了数据集增强，侧重于丰富情感和上下文线索；然而，这些方法对历史语言分析的影响有限。第二种策略，即半自动注释过程，有效解决了类别不平衡问题，并用高质量注释扩充了数据集。尽管反讽的复杂性带来了挑战，这项工作通过两个关键贡献推动了情感分析的发展：引入了一个新的历史西班牙语数据集，用于情感分析和反讽检测的标注；并提出了一种半自动注释方法，其中人类专业知识对于优化LLMs结果至关重要，通过将历史和文化背景作为核心特征加以丰富。
英文摘要：
This study explores the use of large language models (LLMs) to enhance datasets and improve irony detection in 19th-century Latin American newspapers. Two strategies were employed to evaluate the efficacy of BERT and GPT-4o models in capturing the subtle nuances nature of irony, through both multi-class and binary classification tasks. First, we implemented dataset enhancements focused on enriching emotional and contextual cues; however, these showed limited impact on historical language analysis. The second strategy, a semi-automated annotation process, effectively addressed class imbalance and augmented the dataset with high-quality annotations. Despite the challenges posed by the complexity of irony, this work contributes to the advancement of sentiment analysis through two key contributions: introducing a new historical Spanish dataset tagged for sentiment analysis and irony detection, and proposing a semi-automated annotation methodology where human expertise is crucial for refining LLMs results, enriched by incorporating historical and cultural contexts as core features.
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。
