Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated as OUTSIDE (O), and they are undesirably pushed apart to other entities that are not labeled as OUTSIDE (O) by the current contrastive learning method end up with a noisy prototype for the semantic representation of the label, though there are many OUTSIDE (O) labeled entities are relevant to the labeled entities. To address this challenge, we propose a novel method named Weighted Prototypical Contrastive Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our approach primarily revolves around constructing the prototype-based contractive loss and weighting network. These components play a crucial role in assisting the model in differentiating the negative samples from OUTSIDE (O) tokens and enhancing the discrimination ability of contrastive learning. Experimental results show that our proposed W-PROCER framework significantly outperforms the strong baselines on the three medical benchmark datasets.

翻译：对比学习已成为少样本命名实体识别（NER）的流行解决方案。传统配置力求缩小具有相同标签的token之间的间距，同时增大具有不同标签的token之间的距离。然而，在医学领域，存在大量标注为OUTSIDE (O)的实体，当前的对比学习方法会将这些实体与未标注为OUTSIDE (O)的其他实体不理想地推离，导致标签语义表示的原型存在噪声，尽管许多标注为OUTSIDE (O)的实体与标注实体具有相关性。为应对这一挑战，我们提出了一种名为加权原型对比学习用于医学少样本命名实体识别（W-PROCER）的新方法。我们的方法主要围绕构建基于原型的对比损失和加权网络展开。这些组件在帮助模型区分来自OUTSIDE (O) token的负样本、增强对比学习的判别能力方面发挥着关键作用。实验结果表明，我们提出的W-PROCER框架在三个医学基准数据集上显著优于强基线方法。