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Named Entity Recognition (NER) frequently suffers from the problem ofinsufficient labeled data, particularly in fine-grained NER scenarios. Although$K$-shot learning techniques can be applied, their performance tends tosaturate when the number of annotations exceeds several tens of labels. Toovercome this problem, we utilize existing coarse-grained datasets that offer alarge number of annotations. A straightforward approach to address this problemis pre-finetuning, which employs coarse-grained data for representationlearning. However, it cannot directly utilize the relationships betweenfine-grained and coarse-grained entities, although a fine-grained entity typeis likely to be a subcategory of a coarse-grained entity type. We propose afine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leveragethe hierarchical structure explicitly. In addition, we present an inconsistencyfiltering method to eliminate coarse-grained entities that are inconsistentwith fine-grained entity types to avoid performance degradation. Ourexperimental results show that our method outperforms both $K$-shot learningand supervised learning methods when dealing with a small number offine-grained annotations.

Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets