在低资源领域提升命名实体识别效果，实验验证数据增强的有效性与灵活性。
An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains
- 采用提及替换和上下文词替换两种文本增强方法
- 小规模数据集上增强效果显著，但最优增强数量因任务而异
- 适合低资源领域研究者参考，尤其关注数据稀缺场景
命名实体识别（NER）通常依赖标注数据进行监督学习，但在医疗、法律、金融等专业领域，高质量标注数据稀缺，形成低资源领域。这些领域常包含长尾实体，数据获取困难。为此，本文评估了两种主流文本增强技术——提及替换（Mention Replacement）与上下文词替换（Contextual Word Replacement）在两个典型NER模型（Bi-LSTM+CRF 和 BERT）上的表现。实验基于四个低资源领域数据集，考察不同训练子集大小与增强样本数量的组合影响。结果表明，数据增强在小数据集上尤为有效，但不存在通用最优增强数量；因此，实践者需针对具体任务探索最佳增强量。
原文摘要 · Abstract (English)
Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity of available data. To address this, data augmentation techniques are increasingly being employed to generate additional training instances from the original dataset. In this study, we evaluate the effectiveness of two prominent text augmentation techniques, Mention Replacement and Contextual Word Replacement, on two widely-used NER models, Bi-LSTM+CRF and BERT. We conduct experiments on four datasets from low-resource domains, and we explore the impact of various combinations of training subset sizes and number of augmented examples. We not only confirm that data augmentation is particularly beneficial for smaller datasets, but we also demonstrate that there is no universally optimal number of augmented examples, i.e., NER practitioners must experiment with different quantities in order to fine-tune their projects.
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