W-procer：用于医疗少样本命名实体识别的加权原型对比学习
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition
AI总结：
针对医疗少样本命名实体识别中外部（O）实体被错误推开导致原型噪声的问题，提出加权原型对比学习方法W-PROCER，通过构建基于原型的对比损失和加权网络增强判别能力，在三个医疗基准数据集上显著优于强基线。
摘要：
对比学习已成为少样本命名实体识别（NER）的流行解决方案。常规配置致力于减小具有相同标签的 token 之间的距离，并增大具有不同标签的 token 之间的距离。然而，在医疗领域，这种设置的效果可能会受到影响，因为存在大量被标注为外部（O）的实体，当前的对比学习方法会将其不理想地推开至未被标注为外部（O）的其他实体，最终导致标签的语义表示原型充满噪声，尽管许多被标注为外部（O）的实体与已标注实体相关。为应对这一挑战，我们提出了一种名为用于医疗少样本命名实体识别的加权原型对比学习（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。这些组件在协助模型区分负样本与外部（O）token，并增强对比学习的判别能力方面发挥着至关重要的作用。实验结果表明，我们提出的 W-PROCER 框架在三个医疗基准数据集上显著优于强基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
