Leveraging Continuous Prompt for Few-Shot Named Entity Recognition in Electric Power Domain with Meta-Learning

Conventional named entity recognition methods usually assume that the model can be trained with sufficient annotated data to obtain good recognition results.However,in Chinese named entity recognition in the electric power domain,existing methods still face the challenges of lack of annotated data and new entities of unseen types.To address these challenges,this paper proposes a meta-learning-based continuous cue adjustment method.A generative pre-trained language model is used so that it does not change its own model structure when dealing with new entity types.To guide the pre-trained model to make full use of its own latent knowledge,a vector of learnable parameters is set as a cue to compensate for the lack of training data.In order to further improve the model's few-shot learning capability,a meta-learning strategy is used to train the model.Experimental results show that the proposed approach achieves the best results in a few-shot electric Chinese power named entity recognition dataset compared to several traditional named entity approaches.