W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition

TL;DR

This paper introduces W-PROCER, a novel weighted prototypical contrastive learning method that improves medical few-shot NER by better handling OUTSIDE tokens, leading to significant performance gains.

Contribution

The paper proposes a new weighted contrastive learning approach with prototype-based loss and weighting network for medical few-shot NER, addressing noise from OUTSIDE tokens.

Findings

W-PROCER outperforms strong baselines on three medical datasets.

The method effectively differentiates negative samples from OUTSIDE tokens.

Significant improvement in NER accuracy in few-shot medical scenarios.

Abstract

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.