地球科学的研究成果通常记录在技术报告、期刊论文、书籍等文献中,但许多详细的地球科学报告未被使用,这为信息提取提供了机遇.为此,我们提出了一种名为GMNER(Geological Minerals named entity recognize,MNER)的深度神经网络模型,用于识别和提取矿物类型、地质构造、岩石与地质时间等关键信息.与传统方法不同,本次采用了大规模预训练模型 BERT(Bidirectional Encoder Representations from Transformers,BERT)和深度神经网络来捕捉上下文信息,并结合条件随机场(Conditional random field,CRF)以获得准确结果.实验结果表明,MNER模型在中文地质文献中表现出色,平均精确度为0.898 4,平均召回率0.922 7,平均Fl分数0.9104.研究不仅为自动矿物信息提取提供了新途径,也有望促进矿产资源管理和可持续利用.
Geological Mineral Attribute Recognition Method Based on Large-Scale Pre-Trained Model and Its Application
Geoscience research results are usually documented in technical reports,journal papers,books,and other lit-erature;however,many detailed geoscience reports are unused,which provides challenges and opportunities for informa-tion extraction.To this end,we propose a deep neural network model called GMNER(Geological Minerals named entity recognize,MNER)for recognizing and extracting key information such as mineral types,geological formations,rocks,and geological time.Unlike traditional methods,we employ a large-scale pre-trained model BERT(Bidirectional Encod-er Representations from Transformers,BERT)and deep neural network to capture contextual information and combine it with a conditional random field(CRF)to obtain more accurate and accurate information.The experimental results show that the MNER model performs well in Chinese geological literature,achieving an average precision of 0.8984,an aver-age recall of 0.9227,and an average Fl score of 0.9104.This study not only provides a new way for automated mineral information extraction but also is expected to promote the progress of mineral resource management and sustainable utili-zation.
Mineral information extractionDeep neural networkMineral documentationNamed entity recognition