Abstract: Aiming at the key information extraction from fire accident investigation files, we propose a BERT-CRF based named entity recognition method for obtaining information such as accident location, cause and effect, safety measure, etc. We firstly construct a fire accident text corpus by annotating 161 accident reports and using a data augmentation method on the labeled data. Then according to BERT pre?training model, the bidirectional feature extraction method is performed on the sentence sequences in the corpus. Extracted information of the accident text context is further predicted as the key entities by CRF model and the entity label transfer rules. Experiments show that the precision, recall and F1 values of the BERT-CRF model in the fire accident case named entity recognition task are 76.36%, 86.19%, and 80.97%, respectively, which are better than BERT and BERT-BiLSTM-CRF models, and the training time is 61 seconds shorter than that of model BERT-BiLSTM-CRF. Our final model can provide accurate entity construction services for downstream systems such as fire investigation knowledge base and file compilation.

Key words: named entity recognition, BERT-CRF, fire accident, fire information, fire investigation file, text corpus, fire accident text