郑州大学学报（理学版）2026，Vol.58Issue(4)：44-51,8.DOI:10.13705/j.issn.1671-6841.2024175
结合对抗训练的双通道特征融合命名实体识别
Dual-channel Feature Fusion Named Entity Recognition Based on Adversarial Training
摘要
Abstract
Existing named entity recognition(NER)models often lack focus on local information and face challenges in handling long-range dependencies and complex sequence data.To address these is-sues,a dual-channel feature fusion NER model incorporating adversarial training was proposed.First,features were extracted using a pretrained model,with adversarial training enhancing robustness and gen-eralization.Then,a dual-channel module,comprising multi-head attention and time steps,was intro-duced after a bidirectional long short-term memory network to capture both global and local information,improving the model's ability to manage long-range dependencies and complex sequences.Finally,GlobalPointer and rotary position encoding were employed to identify the head and tail information of enti-ties,reducing computational costs while further enhancing the model's ability to capture global informa-tion.Experimental results showed that the proposed model achieved improvements in F1 scores across five public datasets,validating its effectiveness in capturing both global and local information and han-dling complex sequence data.关键词
对抗训练/多头注意力/双通道特征融合/旋转位置编码/命名实体识别Key words
adversarial training/multi-head attention/dual-channel feature fusion/rotary position embedding/named entity recognition分类
信息技术与安全科学引用本文复制引用
李卫军,丁建平,刘雪洋,王子怡,刘世侠,苏易礌..结合对抗训练的双通道特征融合命名实体识别[J].郑州大学学报（理学版）,2026,58(4):44-51,8.基金项目
宁夏高等学校科学研究项目(NYG2024086) （NYG2024086）
中央高校基本科研业务费(2022PT_S04) （2022PT_S04）
国家自然科学基金项目(62066038,61962001) （62066038,61962001）
