Abstract:
The large amount of accident information in the accident case database can provide rich and valuable experience for the design of safety related system, including time, location, cause, process of accidents, etc. These informations play an important role in hazard identification, but they are usually distributed in various paragraphs of accident documents, which makes manual extraction inefficient and costly. This paper proposes a text classification method for accident cases based on BERT pre-training model, which can classify accident case texts into four categories: ACCIDENT、CAUSE、CONSEQUENCE, and RESPONSE. In addition, a test dataset of accident cases is collected and produced for training the model. The experiment shows that this method can achieve the automatic classification of accident case text, with a classification accuracy of 73.44%, a recall rate of 69.13%, and an F1 value of 0.71. In this paper, multiple groups of different experimental parameters are set up, and the effect of parameter settings on classification is fully explored through experiments to find the best parameter settings. The proposed classification method can help better mine the semantic information in the accident case text and provide powerful technical support for the subsequent establishment of expert knowledge base and efficient accident retrieval platform.