Despite the recent success achieved by several two-stage prototypical
networks in few-shot named entity recognition (NER) task, the over-detected
false spans at span detection stage and the inaccurate and unstable prototypes
at 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.

该研究提出了一种新的基于类型感知的分解框架 TadNER，在将支持样本和类型名称作为参照物构建更准确、更稳定的原型时，通过提出类型感知的跨度过滤策略和对比学习策略来解决目前几乎所有两阶段典型化网络在少样本 NER 任务中所遇到的具有挑战性的问题，并在各种基准测试中证明了 TadNER 的优越性。