Named Entity Recognition Method for Unsafe Underground Behaviors in Coal Mines

A coal mine unsafe behavior corpus containing 8 entity categories and 2 359 samples has been constructed using a BIO labeling strategy to improve the efficiency of underground safety management and realize safe coal mine production, based on the relevant standards and norms of the coal mine industry as well as insights into the field of underground unsafe behavior. Aiming at the problems of insufficient semantic information utilization, unbalanced entity distribution, and fuzzy entity boundary in the named entity recognition task of unsafe behavior in coal mines, this study proposes a named entity recognition model based on Global Pointer and adversarial training. First, the improved hierarchical RoBERTa model is used to make full use of multi-layer semantic information to enhance the text vectorization of underground unsafe behavior, and the word embedding layer is disturbed by adversarial training to alleviate the problem of data imbalance and enhance model robustness. Second, Bidirectional Gated Recurrent Unit (BiGRU) is used in the feature extraction layer to more effectively capture the contextual semantic features of the corpus and enhance the semantic association of the text. Finally, Global Pointer is constructed in the decoding layer to obtain more accurate entity boundary recognition results. The effectiveness of the proposed model is evaluated on a self-built small sample coal mine underground unsafe behavior dataset. The results show that the accuracy, recall, and F1 value of the proposed model are 78.77%, 78.20%, and 78.48%, respectively, which are 2.27, 0.63, and 1.45 percentage points higher than those of the BERT-Global Pointer model. The findings provide a basis for constructing a knowledge graph of unsafe behavior in underground mines.