  This paper proposes an automatic Chinese text categorization method for
solving the emergency event report classification problem. Since bidirectional
encoder representations from transformers (BERT) has achieved great success in
natural language processing domain, it is employed to derive emergency text
features in this study. To overcome the data imbalance problem in the
distribution of emergency event categories, a novel loss function is proposed
to improve the performance of the BERT-based model. Meanwhile, to avoid the
impact of the extreme learning rate, the Adabound optimization algorithm that
achieves a gradual smooth transition from Adam to SGD is employed to learn
parameters of the model. To verify the feasibility and effectiveness of the
proposed method, a Chinese emergency text dataset collected from the Internet
is employed. Compared with benchmarking methods, the proposed method has
achieved the best performance in terms of accuracy, weighted-precision,
weighted-recall, and weighted-F1 values. Therefore, it is promising to employ
the proposed method for real applications in smart emergency management
systems.
