摘要
在雷达维修领域的知识图谱构建中,由于其专业性强、标注数据集稀缺,命名实体识别模型训练存在较大困难,传统模型训练效果达不到应用要求。在Bi GRU-CRF模型的基础上引入了预训练模型,提出了ERNIE-Bi GRU-CRF模型。首先,以某型号雷达为例,收集维修数据,并进行数据的预处理,同时使用doccano平台对数据进行人工标注,获得雷达维修领域命名实体识别数据1100余条。然后,通过ERNIE预训练模型获取雷达维修训练数据的动态词向量,Bi GRU获取双向语义信息。最后,通过CRF约束得到最合理的序列标注结果。实验结果表明,在少量训练语料的条件下,所提模型具有较强的识别效果,相比于Bi GRU-CRF、Bi LSTM-CRF模型,其F1值有一定提升,有效解决了雷达维修领域训练语料缺乏、训练效果不佳的问题,在雷达维修领域知识图谱的自动化构建中具有一定的实用价值。
In the construction of a knowledge graph for radar maintenance,the strong specialization and scarcity of annotated datasets pose significant challenges in training named entity recognition(NER)models,with traditional model training failing to meet application requirements.Building upon the BiGRU-CRF model,this paper introduces a pre-trained model and proposes the ERNIE-BiGRU-CRF model.First,taking a specific radar model as an example,maintenance data were collected and preprocessed.The doccano platform was used for manual annotation,resulting in over 1,100 labeled NER data entries in the radar maintenance domain.Next,dynamic word embeddings for the radar maintenance training data were obtained using the ERNIE pre-trained model,while BiGRU captured bidirectional semantic information.Finally,the most reasonable sequence labeling results were derived through CRF constraints.Experimental results show that,with limited training data,the proposed model achieves strong recognition performance.Compared to BiGRU-CRF and BiLSTM-CRF models,it demonstrates an improvement in F 1-score,effectively addressing the issues of insufficient training data and suboptimal training performance in the radar maintenance domain.This model holds practical value for the automated construction of knowledge graphs in radar maintenance.
作者
曾垂振
崔良中
马文卓
ZENG Chuizhen;CUI Liangzhong;MA Wenzhuo(College of Electronic Engineering,Naval University of Engineering,Wuhan 430033;Ordnance NCO Academy,Army Engineering University of PLA,Wuhan 430075,China)
出处
《计算机工程与科学》
北大核心
2025年第6期1106-1113,共8页
Computer Engineering & Science
关键词
雷达维修
命名实体识别
ERNIE模型
大语言模型
radar maintenance
named entity recognition
enhanced representation through knowledge integration(ERNIE)model
large language model
