摘要
【目的】针对英文文本中存在多重嵌套和实体语义不明确的问题,提出一种融合单词多元信息的嵌套命名实体识别方法GTR-NNER。【方法】基于三仿射注意力引导的图卷积网络模块融合单词信息、单词位置信息、单词边界信息、单词标签信息以及语法信息,根据得到的多元信息进行跨度枚举,最后通过判别器完成实体识别。【结果】在两个嵌套数据集上进行10折交叉验证,GTR-NNER方法的平均F1值分别为84.38%和91.44%;在两个非完全嵌套数据集GENIA和ACE2005上,GTR-NNER方法的F1值分别为82.19%和89.27%。【局限】融合单词的多元信息致使模型的收敛速度变慢。【结论】在命名实体识别模型中结合单词的多元信息能够提高嵌套实体识别的效果,且实验结果证明本文融合单词多元信息的方法是有效的。
[Objective]To address the challenges of multiple nested entities and semantic ambiguity in English texts,this study proposes a nested entity recognition method named GTR-NNER,which integrates multi-dimensional word information.[Methods]The proposed method employs a triaffine attention-guided graph convolutional network(GCN)module to integrate multiple types of word information,including word content,word position,word boundary,word label,and syntactic information.Based on the extracted multi-dimensional information,span enumeration is performed,followed by entity recognition through a discriminator.[Results]The proposed GTR-NNER method achieves an average F1 score of 84.38%and 91.44%on two nested NER datasets through 10-fold cross-validation.Additionally,on two partially nested datasets,GENIA and ACE2005,it attains F1 scores of 82.19%and 89.27%,respectively.[Limitations]The integration of multi-dimensional word information slows down the model’s convergence speed.[Conclusions]Incorporating multi-dimensional word information into NER models effectively enhances the performance of nested entity recognition.
作者
余肖生
黄莺
张云涛
陈鹏
Yu Xiaosheng;Huang Ying;Zhang Yuntao;Chen Peng(Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,China Three Gorges University,Yichang 443002,China;College of Computer and Information,China Three Gorges University,Yichang 443002,China)
出处
《数据分析与知识发现》
北大核心
2025年第3期127-135,共9页
Data Analysis and Knowledge Discovery
基金
国家重点研发计划资助项目(项目编号:2016YFC0802500)的研究成果之一。
关键词
嵌套命名实体识别
单词多元信息
图卷积网络
Nested Named Entity Recognition
Multi-dimensional Word Information
Graph Convolutional Network
