针对历史文本中极稀有事件提取难题，提出反向优先专家模型提升小众事件识别效果。
ROBE: Reversed-Order-Biased-Experts for Extracting Extreme Long-tail Events from Historical Texts
- 构建分组专家模型，按频次或语义相似性划分事件类别，赋予低频类更高预测优先级。
- 在荷兰17-18世纪历史语料上实现召回率提升0.10、精确率提升0.16，长尾类别F1提升0.10。
- 适合处理小样本、低频事件的文本挖掘任务，尤其适用于历史、档案等冷门领域研究者。
本文提出方法，从涵盖17至18世纪的荷兰历史语料库中提取超过50种事件类型。所提方法旨在解决一个近乎不可能的任务：提取‘长尾中的长尾’。19世纪前的历史数据本身属于小众领域，未被大型语言模型预训练覆盖，且训练数据中相关事件标注极为稀疏。为此，我们为训练数据中存在的事件子集构建专家分类器，分组依据为相似出现频率或语义相关性。对代表性不足的事件类别，其专家模型在预测时获得更高优先级，以避免被频率偏差主导。该新型分类器融合策略称为ROBE（Reversed-Order-Biased-Experts），专为保护长尾而设计。同时提出一种受控生成领域特定合成数据的方法。两种ROBE实现均优于简单微调编码器模型，分别带来0.10的召回率提升和0.16的精确率提升；最佳模型在该小众数据集的长尾类别上实现0.10的F1提升。
原文摘要 · Abstract (English)
This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries. The methods we propose aim to tackle the impossible: extracting the long-tail of the long-tail. Historic data from before the 19th century is in itself a niche domain not covered in the pre-training of Large Language Models, and we aim to extract events only very scarcely annotated in the training data available for this domain. We propose creating expert classifiers for subgroups of the events present in the training data. We make these groupings based on similar frequency in the training data or on semantic relatedness. Experts trained on underrepresented events are assigned higher priority when predicting to avoid being dominated by frequency biases. We refer to this new way of combining classifiers, specifically tailored to protect the long-tail, as ROBE: Reversed-Order-Biased-Experts. We also propose a controlled method to create domain-specific synthetic data. Our two implementations of ROBE outperform a simple fine-tuned encoder model with a .10 increase in recall and a .16 increase in precision respectively. The best model achieves a .10 increase in f1 for a group of long-tail classes in our niche data set.
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