The Optimal Sampling Strategy for Few-shot Named Entity Recognition Based with Prototypical Network

Few-shot Named Entity Recognition (NER) is the task of identifying and classifying entities under conditions of low resources. The preceding approach relies on meta-learning, wherein the classification model is trained through N-way K-shot. However, It neglects essential information: the model’s input is organized at the sentence level. To tackle the aforementioned issues, we introduce a sampling strategy — the Loose Nway K˜shot sampling algorithm. This approach combines the number of entities and sentence level which is the smallest unit of NER models’ input. Experimental results demonstrate that our strategy currently stands as the most effective method for Few-shot NER. Furthermore, our investigation reveals that various classes of entities within the same sentence can impede the prototype representation of each class. Consequently, we introduce a training method that involves utilizing sentences with single entity class for pre-training purposes. The experimental outcomes substantiate that this training methodology maximizes the utilization of labeled data, enabling the pre-training model to swiftly adapt to new domains. This approach significantly enhances the performance of NER.