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.

翻译：尽管近期两阶段原型网络在少样本命名实体识别（NER）任务中取得了成功，但跨度检测阶段过度检测的虚假跨度以及类型分类阶段不准确且不稳定的原型仍是具有挑战性的问题。本文提出一种新颖的类型感知解耦框架TadNER来解决这些问题。我们首先提出一种类型感知跨度过滤策略，通过移除与类型名称语义距离过远的跨度来滤除虚假跨度，进而提出一种类型感知对比学习策略，通过联合利用支持样本和类型名称作为参考来构建更准确稳定的原型。在多个基准数据集上的大量实验证明，所提出的TadNER框架取得了新的最优性能。我们的代码和数据将在https://github.com/NLPWM-WHU/TadNER 公开。