Research on NER model for coal mine safety hazards based on BERT-CNN-BiGRUs-CRF

• Main Authors: Ma, Li, Yang, Fan, Dai, Xinguan, Gao, Hangbiao, Song, Shuang
• Format: Conference Proceeding
• Language: English
• Published: SPIE 16.10.2023
• ISBN: 1510668624; 9781510668621
• ISSN: 0277-786X
• DOI: 10.1117/12.3009528

During the coal mine safety production process, a significant amount of text data containing information about coal mine safety hazards, such as working face, hazard location, hazard subject, and hazard problem description, is accumulated. The extraction of named entities from coal mine safety hazard text serves as the foundation for conducting a study on the early detection of coal mine safety hazards. Since the single modeling strategy is being used, the current named entity recognition (NER) model technique has a low recognition precision and ratio. Firstly, a character-level encoding of coal mine safety hazard text is performed by a BERT pre-training language model to generate word vectors based on contextual information, followed by local and global deep feature extraction of coal mine safety hazard word vectors by a convolutional neural network (CNN) with multi-layer bi-directional gated recurrent neural networks (BiGRUs), and finally decoding by conditional random fields (CRF) to generate global optimal label. On tasks of the NER for coal mine safety hazards, by comparing and analyzing with the mainstream deep learning entity recognition models, As shown by the outcomes that the precision of the NER model for coal mine safety hazards proposed in this paper reaches 91.74%, the recall reaches 93.20%, and the 𝐹1-measure reaches 92.45%, which shows a better performance. The NER task of precisely obtaining key information such as hazard location and hazard subject from unstructured coal mine safety hazards text data is achieved, which provides important information for hazard investigation and management.