重庆理工大学学报2024，Vol.38Issue(21)：206-212,7.DOI:10.3969/j.issn.1674-8425(z).2024.11.026
火电厂故障诊断文本的实体抽取模型构建
Construction of entity extraction model for fault diagnosis text in thermal power plants
摘要
Abstract
To address such issues as blurred entity boundaries,insufficient text features,and unremarkable model recognition effects in the field of fault diagnosis for thermal power plants,we propose a text entity recognition model based on improved BERT-BiLSTM-CRF for fault diagnosis.Entity naming recognition experiments are conducted on a newly built dataset.Our results indicate the entity recognition model based on the improved BERT-BiLSTM-CRF achieves an F1 score of 0.901 6,which is superior to those of other models,validating the effectiveness of our model.To enhance the performance of the BERT model in a Chinese context,model parameters are optimized,and adversarial training methods are employed to improve model accuracy,which is up by 0.020 6 in F1 score.关键词
实体命名识别/预训练语言模型/火电厂/故障诊断/对抗训练Key words
entity naming recognition/pre-trained language model/thermal power plants/fault diagnosis/adversarial training分类
信息技术与安全科学引用本文复制引用
陈宏,王云博,穆思澎,陈阳..火电厂故障诊断文本的实体抽取模型构建[J].重庆理工大学学报,2024,38(21):206-212,7.基金项目
国家自然科学基金项目(52275138) （52275138）
