Chinese Named Entity Recognition for Automobile Fault Texts Based on External Context Retrieving and Adversarial Training

Identifying key concepts in automobile fault texts is crucial for understanding fault causes and enabling diagnosis. However, effective mining tools are lacking, leaving much latent information unexplored. To solve the problem, this paper proposes Chinese named entity recognition for automobile fault texts based on external context retrieval and adversarial training. First, we retrieve external contexts by using a search engine. Then, the input sentence and its external contexts are respectively fed into Lexicon Enhanced BERT to improve the text embedding representation. Furthermore, the input sentence and its external contexts embedding representation are fused through the attention mechanism. Then, adversarial samples are generated by adding perturbations to the fusion vector representation. Finally, the fusion vector representation and adversarial samples are input into the BiLSTM-CRF layer as training data for entity labeling. Our model is evaluated on the automotive fault datasets, Weibo and Resume datasets, and achieves state-of-the-art results.