Research on Automatic Classification of Mine Safety Hazards Using Pre-Trained Language Models

The advancement of pre-trained language models (PLMs) has provided new avenues for addressing text classification challenges. This study investigates the applicability of PLMs in the categorization and automatic classification of short-text safety hazard information specifically within mining industry contexts. Leveraging the superior word embedding capabilities of encoder-based PLMs, the standardized hazard description data collected from mine safety supervision systems were vectorized while preserving semantic information. Utilizing the BERTopic model, the study successfully mined hazard category information, which was subsequently manually consolidated and labeled to form a standardized dataset for training classification models. A text classification framework based on both encoder and decoder models was designed, and the classification outcomes were compared with those from ensemble learning models constructed using Naive Bayes, XGBoost, TextCNN, etc. The results demonstrate that decoder-based PLMs exhibit superior classification accuracy and generalization capabilities for semantically complex safety hazard descriptions, compared to Non-PLMs and encoder-based PLMs. Additionally, the study concludes that selecting a classification model requires a comprehensive consideration of factors such as classification accuracy and training costs to achieve a balance between performance, efficiency, and cost. This research offers novel insights and methodologies for short-text classification tasks, particularly in the application of PLMs in mine safety management and hazard analysis, laying a foundation for subsequent related studies and further improvements in mine safety management practices.