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）的热门解决方案。传统配置致力于缩小相同标签词元间的距离，同时增大不同标签词元间的距离。然而，在医学领域，大量实体被标注为“外部（O）”，当前对比学习方法会将它们与其他未被标注为“外部（O）”的实体不当地分离开来，导致标签语义表示的原型产生噪声——尽管许多“外部（O）”标注实体与标注实体存在关联。为解决这一挑战，我们提出一种名为“面向医学少样本命名实体识别的加权原型对比学习”（W-PROCER）的新方法。该方法主要围绕构建基于原型的对比损失函数及加权网络展开。这些组件在帮助模型区分“外部（O）”词元中的负样本、增强对比学习判别能力方面发挥关键作用。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模型。