Abstract:

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.

Key words: unsafe underground behavior, named entity recognition, RoBERTa model, adversarial training, Global Pointer model

摘要：

为提高井下安全管理效率, 实现煤矿安全生产, 根据煤矿行业相关标准规范, 并结合井下不安全行为领域知识, 采用BIO标注策略构建一个包含8类实体类别、2 359条样本的煤矿井下不安全行为语料库。针对煤矿井下不安全行为命名实体识别任务中存在的语义信息利用不足、实体分布不均衡、实体边界模糊的问题, 提出一种基于Global Pointer和对抗训练的煤矿井下不安全行为命名实体识别模型。首先, 采用改进的分层RoBERTa模型并利用多层语义信息增强井下不安全行为文本向量化, 结合对抗训练对词嵌入层进行扰动, 缓解数据不平衡问题, 增强模型的鲁棒性; 其次, 在特征提取层采用双向门控循环单元(BiGRU)可以更有效地捕获语料的上下文语义特征, 加强文本语义关联; 最后, 在解码层构造Global Pointer, 获得更准确的实体边界识别结果。为验证提出模型的有效性, 在自建的小样本煤矿井下不安全行为数据集上进行实验, 结果表明, 该模型的精确率、召回率和F1值分别为78.77%、78.20%、78.48%, 相比于BERT-Global Pointer模型分别提高了2.27、0.63、1.45百分点, 为构建井下不安全行为知识图谱提供基础。

关键词: 井下不安全行为, 命名实体识别, RoBERTa模型, 对抗训练, Global Pointer模型