AutoDes: Few-Shot Named Entity Recognition with Class Descriptions.

Few-shot named entity recognition is extremely important for the domain lacking annotation data. Existing approaches ignore that class descriptions can provide additional information and rich prior knowledge for the model. Most datasets do not provide class description information, and the same entity class may have different definitions in different datasets. In this paper, we proposed a simple and effective method, i.e., AutoDes, that can extract class descriptions from annotated data automatically. AutoDes uses examples to construct class descriptions and takes the prediction words of entity-oriented prompts as candidate examples. Experiments with different few-shot settings on multiple datasets show that AutoDes is superior to the state-of-the-art methods in low-resource settings, improving F1 scores by 1.2% to 7.5% absolute points.