  Despite the recent success achieved by several two-stage prototypical
networks in few-shot named entity recognition (NER) task, the overdetected
false spans at the span detection stage and the inaccurate and unstable
prototypes at the type classification stage remain to be challenging problems.
In this paper, we propose a novel Type-Aware Decomposed framework, namely
TadNER, to solve these problems. We first present a type-aware span filtering
strategy to filter out false spans by removing those semantically far away from
type names. We then present a type-aware contrastive learning strategy to
construct more accurate and stable prototypes by jointly exploiting support
samples and type names as references. Extensive experiments on various
benchmarks prove that our proposed TadNER framework yields a new
state-of-the-art performance. Our code and data will be available at
https://github.com/NLPWM-WHU/TadNER.
