舰船电子工程2025，Vol.45Issue(5)：23-27,106,6.DOI:10.3969/j.issn.1672-9730.2025.05.006
基于数据增强的作战体系命名实体识别研究
Research on Named Entity Recognition in Operation System Based on Data Augmentation
王小龙 1王暖臣 2穆歌 2阎晓培 2李新津2
作者信息
- 1. 军事科学院系统工程研究院 北京 100141||93236部队 北京 100085
- 2. 军事科学院系统工程研究院 北京 100141
- 折叠
摘要
Abstract
Named entity recognition(NER)for warfare systems encounters challenges such as difficulties in complex text structures,ambiguous entity boundaries,and imbalanced entity counts.To address these issues,a data augmentation-based NER method for warfare systems is proposed.Firstly,a corpus of warfare systems is constructed based on open-source internet data.The entity types of warfare systems are determined according to the United States department of defense architecture framework(DODAF)meta-model,and annotation rules are standardized in combination with corpus characteristics.Secondly,a deep learn-ing model based on RoBERTa-BILSTM-CRF is constructed for entity recognition within warfare systems.Finally,a data augmenta-tion approach leveraging large language model(LLM)is designed to enhance the original data from both grammatical and semantic perspectives,addressing the issue of imbalanced entity quantities in the warfare system corpus and improving the model's recogni-tion performance.Experimental results demonstrate that the model trained on the augmented dataset exhibits improvements in accu-racy,recall and F1 score.关键词
实体识别/大语言模型/数据增强/深度学习Key words
named entity recognition/LLM/data augmentation/deep learning分类
天文与地球科学引用本文复制引用
王小龙,王暖臣,穆歌,阎晓培,李新津..基于数据增强的作战体系命名实体识别研究[J].舰船电子工程,2025,45(5):23-27,106,6.
