SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics

Few-shot named entity recognition requires sufficient prior knowledge to transfer valuable knowledge to the target domain with only a few labeled examples. Existing Chinese few-shot named entity recognition methods suffer from inadequate prior knowledge and limitations in feature representation. In this paper, we utilize enhanced Span and Label semantic representations for Chinese few-shot Named Entity Recognition (SLNER) to address the problem. Specifically, SLNER utilizes two encoders. One encoder is used to encode the text and its spans, and we employ the biaffine attention mechanism and self-attention to obtain enhanced span representations. This approach fully leverages the internal composition of entity mentions, leading to more accurate feature representations. The other encoder encodes the full label names to obtain label representations. Label names are broad representations of specific entity categories and share similar semantic meanings with entities. This similarity allows label names to offer valuable prior knowledge in few-shot scenarios. Finally, our model learns to match span representations with label representations. We conducted extensive experiments on three sampling benchmark Chinese datasets and a self-built food safety risk domain dataset. The experimental results show that our model outperforms the F1 scores of 0.20-6.57% of previous state-of-the-art methods in few-shot settings.

Automated summary

In this paper, an enhanced Span and Label semantic representation method is proposed for Chinese few-shot Named Entity Recognition (SLNER). The method uses two encoders, one to encode the text and its spans to obtain enhanced span representations, and the other to encode the label names to obtain label representations. The model learns to match span representations with label representations, and experimental results show that it outperforms previous state-of-the-art methods in few-shot settings.