  Few-shot named entity recognition (NER) aims at identifying named entities
based on only few labeled instances. Most existing prototype-based sequence
labeling models tend to memorize entity mentions which would be easily confused
by close prototypes. In this paper, we proposed a Prototypical Semantic
Decoupling method via joint Contrastive learning (PSDC) for few-shot NER.
Specifically, we decouple class-specific prototypes and contextual semantic
prototypes by two masking strategies to lead the model to focus on two
different semantic information for inference. Besides, we further introduce
joint contrastive learning objectives to better integrate two kinds of
decoupling information and prevent semantic collapse. Experimental results on
two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the
previous SOTA methods in terms of overall performance. Extensive analysis
further validates the effectiveness and generalization of PSDC.
