关键词: 命名实体识别（NER）, 少样本学习, 提示学习

Abstract: The commonly adopted approach for few-shot named entity recognition (NER) typically involves the use of similarity-based metrics. In order to fully leverage knowledge transfer within the model parameters, this paper proposes a prefix-tuning method for few-shot NER (P-NER). This involves placing the input text’s feature vectors into an embedding module for feature extraction. The vector parameters of prefix prompts are concatenated to the front end of the encoding layer model, with the encoding layer model parameters being fixed. The results obtained from the encoding layer are decoded using a cross-entropy model. For each training sample, two sub-models are sampled, and regularization of the model prediction is achieved by minimizing the relative entropy between the two sub-models. The model’s consistency with actual labels is assessed by validating the output probability and the probability of true labels for each word, ultimately yielding the classification results. Experimental results demonstrate that on the CoNLL2003 dataset, this method achieves an average F1 score of 84.92% for in-domain few-shot entity recognition. In the cross-domain few-shot entity recognition tasks, it outperforms other baseline methods on three datasets: MIT Movie, MIT Restaurant and ATIS. Thus, this method significantly enhances the effectiveness of few-shot named entity recognition with a mere 2.9% adjustment to the parameters of previous fine-tuning methods.

Key words: named entity recognition (NER), few-shot learning, prompt learning