关键词: 多尺度, 核电质量文本, 语义抽取, 预训练语言模型, 条件随机场

Abstract: A semantic extraction method of multi-scale nuclear power quality text fault information was proposed to obtain the information of fault equipment and their stages from nuclear power quality text. The quality text included the faulty equipment and normal equipment， while the whole value chain stages of design， procurement， construction, and commissioning were not described. Firstly， based on Transformer bidirectional encoding， the pre-trained language model were used to convert nuclear equipment quality text into text vectors. The bidirectional gated recurrent unit network with attention mechanism was introduced to mine the key semantic features of quality text defects. On the basis of those above， the conditional random field was used to predict the key semantic features and output the fault equipment. Fine-tuning the extracted key semantic features by multi-layer perceptron， the stages of fault equipment was interpreted. Finally， the experimental verification was conducted based on real nuclear power quality text datasets， and the F1 value reached 94.3%. The results show that the proposed method has good feasibility and effectiveness. 

Key words: multi-scale, nuclear power quality text, semantic extraction, pre-trained language model, conditional random field