基于BERT的数控机床故障领域命名实体识别

针对数控机床(computer numerical control，CNC)故障领域命名实体识别方法中存在实体规范不足及有效实体识别模型缺乏等问题，制定了领域内实体标注策略，提出了一种基于双向转换编码器(bidirectional encoder representations from transformers，BERT)的数控机床故障领域命名实体识别方法。采用BERT编码层预训练，将生成向量输入到双向长短期记忆网络(bidirectional long short-term memory，BiLSTM)交互层以提取上下文特征，最终通过条件随机域(conditional random field，CRF)推理层输出预测标签。实验结果表明，BERT-BiLSTM-CRF模型在数控机床故障领域更具优势，与现有模型相比，F1值提升大于1.85%。

Named entity recognition in the fault field of computer numerical control machine tool based on BERT

Aiming at the problems of insufficient entity specification and lack of effective entity recognition models in the named entity recognition method in the field of computer numerical control (CNC) machine tool faults, an entity labeling strategy in the field was formulated, and a named entity recognition method based on bidirectional encoder representations from transformers (BERT) in the field of CNC machine tool faults was proposed. The BERT coding layer is pre-trained, and the generated vector is input to the bidirectional long short-term memory (BiLSTM) interaction layer to extract contextual features, and finally the predicted label is output through the conditional random field (CRF) inference layer. Experimental results show that BERT-BiLSTM-CRF model has more advantages in the field of CNC machine tool failures. Compared with the existing models, F1 value increased by more than 1.85%.