  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.
