用大模型提升19世纪西语报纸反讽识别，构建新数据集并优化标注流程
Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish
- 结合历史语境设计半自动标注法，融合人类专家判断
- 构建首个19世纪拉美报刊情感与反讽标注数据集
- 适合数字人文、历史文本分析研究者参考
本研究探索大型语言模型（LLMs）在19世纪拉丁美洲报纸中反讽检测的应用。采用两种策略评估BERT和GPT-4o在多分类与二分类任务中的表现：一是通过丰富情感与上下文线索增强数据集，但对历史语言分析效果有限；二是实施半自动标注流程，有效缓解类别不平衡问题，并生成高质量标注数据。尽管反讽识别面临语言复杂性挑战，本工作仍作出两项关键贡献：构建首个针对19世纪西班牙语的带情感与反讽标签的数据集，并提出以历史与文化背景为核心特征的半自动标注方法，强调人类专家在优化模型结果中的关键作用。
原文摘要 · Abstract (English)
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.
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