Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement

• 出版年: Applied Sciences
• 主要な著者: Di Wu, Yao Chen, Mingyue Yan
• フォーマット: 論文
• 言語: 英語
• 出版事項: MDPI AG 2025-05-01
• 主題: few-shot named entity recognition; multi-class label prompt selecting; demonstration prompt; core entity replacement
• オンライン･アクセス: https://www.mdpi.com/2076-3417/15/11/6171

At present, researchers are showing a marked interest in the topic of few-shot named entity recognition (NER). Previous studies have demonstrated that prompt-based learning methods can effectively improve the performance of few-shot NER models and can reduce the need for annotated data. However, the contextual information of the relationship between core entities and a given prompt may not have been considered in these studies; moreover, research in this field continues to suffer from the negative impact of a limited amount of annotated data. A multi-class label prompt selection and core entity replacement-based named entity recognition (MPSCER-NER) model is proposed in this study. A multi-class label prompt selection strategy is presented, which can assist in the task of sentence–word representation. A long-distance dependency is formed between the sentence and the multi-class label prompt. A core entity replacement strategy is presented, which can enrich the word vectors of training data. In addition, a weighted random algorithm is used to retrieve the core entities that are to be replaced from the multi-class label prompt. The experimental results show that, when implemented on the CoNLL-2003, Ontonotes 5.0, Ontonotes 4.0, and BC5CDR datasets under 5-Way <i>k</i>-Shot (<i>k</i> = 5, 10), the MPSCER-NER model achieves minimum <i>F</i>1-score improvements of 1.32%, 2.14%, 1.05%, 1.32%, 0.84%, 1.46%, 1.43%, and 1.11% in comparison with NNshot, StructShot, MatchingCNN, ProtoBERT, DNER, and SRNER, respectively.