Construction of entity extraction model for fault diagnosis text in thermal power plants
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.
entity naming recognitionpre-trained language modelthermal power plantsfault diagnosisadversarial training
国家科技期刊平台
NSTL
万方数据
