计算机工程2026，Vol.52Issue(9)：143-153,11.DOI:10.19678/j.issn.1000-3428.0070449
基于分布校正的小样本命名实体识别方法
Few-Shot Named Entity Recognition Method Based on Distribution Calibration
摘要
Abstract
In the modern industrial sector,the perception and analysis of text data are essential for promoting intelligent manufacturing and optimizing production processes.However,industrial text data are typically characterized by high specialization,diversity,complexity,and annotation costs,making traditional large-scale annotation methods unsuitable.Existing few-shot Named Entity Recognition(NER)methods often use prototypical networks to classify entities,where the prototype is the average of the features of all samples belonging to the same category.However,these methods are highly sensitive to the support set data and prone to sample-selection bias.To address this,a few-shot NER model based on Distribution Calibration called DC-NER is proposed.A two-stage approach for span detection and entity classification is adopted for the few-shot NER task.In the first stage,an entity extractor is used to select candidate entities.In the second stage,a type discriminator assigns these entities to predefined categories.To address the challenge of capturing the true class distribution with a small number of labeled samples in few-shot NER,which leads to inaccurate class prototype calculations,a method is proposed that leverages useful information from the source domain data to calibrate the class distribution in the target domain.Thus,more samples are generated based on the refined class distribution to construct more accurate prototypes,thereby improving the performance in few-shot NER tasks.Experiments on both the in-domain dataset Few-NERD and the cross-domain dataset Cross-NER reveal that DC-NER significantly outperforms the comparative models in terms of F1 value,validating its effectiveness for few-shot NER.关键词
工业文本数据/命名实体识别/小样本学习/原型网络/分布校正Key words
industrial text data/Named Entity Recognition(NER)/few-shot learning/prototypical network/distribution calibration分类
信息技术与安全科学引用本文复制引用
浦震宇,刘志伟,黄勃,何书锋,陈南希,郗文增..基于分布校正的小样本命名实体识别方法[J].计算机工程,2026,52(9):143-153,11.基金项目
华中农业大学农业智能技术教育部工程研究中心开放课题(ERCITA-KF002). （ERCITA-KF002）
