Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios. Although $K$-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels. To overcome this problem, we utilize existing coarse-grained datasets that offer a large number of annotations. A straightforward approach to address this problem is pre-finetuning, which employs coarse-grained data for representation learning. However, it cannot directly utilize the relationships between fine-grained and coarse-grained entities, although a fine-grained entity type is likely to be a subcategory of a coarse-grained entity type. We propose a fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage the hierarchical structure explicitly. In addition, we present an inconsistency filtering method to eliminate coarse-grained entities that are inconsistent with fine-grained entity types to avoid performance degradation. Our experimental results show that our method outperforms both $K$-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations.

翻译：命名实体识别（NER）常面临标注数据不足的问题，尤其在细粒度NER场景中更为突出。尽管$K$-shot学习技术可应用，但其性能在标注数量超过几十个标签后趋于饱和。为解决此问题，我们利用现有提供大量标注的粗粒度数据集。解决此问题的一种直接方法是预微调，即使用粗粒度数据进行表示学习。然而，该方法无法直接利用粗粒度与细粒度实体之间的关系——尽管细粒度实体类型很可能是粗粒度实体类型的子类别。我们提出一种带有细到粗（F2C，Fine-to-Coarse）映射矩阵的细粒度NER模型，以显式利用层级结构。此外，我们提出一种不一致性过滤方法，消除与细粒度实体类型不一致的粗粒度实体，避免性能下降。实验结果表明，在处理少量细粒度标注时，我们的方法优于$K$-shot学习和监督学习方法。