西北工程技术学报2026，Vol.25Issue(1)：27-32,6.DOI:10.26974/j.cnki.XBGC.2026.01.004
融合多头注意力的中文命名实体识别方法
A Chinese Named Entity Recognition Method Integrating Multi-Head Attention
摘要
Abstract
To address the limited feature extraction capacity and the difficulty in modeling long-range dependencies in existing Chinese named entity recognition(NER)models,this study proposed a method that integrates multi-head attention(MA)with a bidirectional gated recurrent unit(BiGRU).Specifically,it first used the ALBERT(a lite BERT)pre-trained language model to generate contextualized representations,which are then fed into a BiGRU to extract global semantic features.A multi-head attention mechanism is subsequently applied to capture long-range dependencies and further enhance the semantic representation then the multi-head attention mechanism is used to capture long-distance dependent information to enhance semantic representations.Finally,a conditional random field(CRF)layer decodes the optimal label sequence.Experimental results show that the proposed method achieves F1 scored above 95%on both the People's Daily and MSRA datasets,outperforming competing models.In addition,compared with the BERT-BiLSTM-CRF model,this approach reduces training time by approximately 14.5%,demonstrating its effectiveness and generalizability.关键词
命名实体识别/多头注意力/双向门控循环单元/条件随机场Key words
named entity recognition/multi-head attention/bidirectional gated recurrent unit/conditional random field分类
信息技术与安全科学引用本文复制引用
胡德洲,李贯峰,李瑞,王云丽,高文馨..融合多头注意力的中文命名实体识别方法[J].西北工程技术学报,2026,25(1):27-32,6.基金项目
国家自然科学基金项目(62066038) （62066038）
宁夏自然科学基金项目(2022AAC03026) （2022AAC03026）
宁夏大学研究生创新项目(CXXM202356) （CXXM202356）
