基于XLNET模型的开阳磷矿成矿条件相关地质实体识别与应用

Recognition and application of geological entities related to ore-forming conditions in the Kaiyang phosphate mine based on the XLNET model

随着磷矿找矿难度越来越大,地质勘探成果报告也愈来愈多,通过人工识别海量文档中与磷矿成矿相关地质信息耗时低效,无法满足知识共享传播和地质报告智能管理的需求。为快速获得磷矿地质文档报告中隐藏的成矿地质知识,基于XLNET模型建立了磷矿成矿地质实体自动识别的方法。首先对实体进行BIO标注建立地质实体字典,利用XLNET作为底层预处理模型学习句子双向语义;然后使用BILSTM-Attention-CRF模型实现文本多标签的智能分类;最后通过定位磷矿实体在报告中的分布位置大致推测该处磷矿成矿条件和成矿模式。将该模型与其余3种模型比较得出结果,该模型识别的准确率(P)、召回率(R)及F1值都接近了90%,较前3种模型分别调高了2%,5%,6%。该研究为开阳磷矿地质研究人员提供了更加高效的地质实体自动识别的方法。

[Objective]With increasing difficulty in phosphate ore prospecting,there are an increasing number of geological exploration reports.The manual recognition of geological information related to phosphate rock mineralization in massive documents is time-consuming and inefficient.It cannot meet the needs of knowledge sharing,dissemination and intelligent management of geological reports.[Methods]To quickly obtain the ore-forming geological knowledge hidden in the phosphate ore reports,this work intends to establish an automatic recognition method for ore-forming geological entities based on the extreme learning machine network(XLNET)model.First,BIO labelling of entities was carried out to establish a geological entity dictionary,and XLNET was used as the underlying preprocessing model to learn the bidirectional semantics of sentences.Then,the BILSTM-Attention-CRF(bidirectional long short term memory(BILSTM)-self attention layer(Attention)-conditional random field(CRF))model was used to realize intelligent classification of multiple text labels.Finally,the ore-forming conditions and ore-forming model of phosphate ore in the reports were roughly predicted by locating the distribution position of phosphate ore entities in the report.[Results]Comparing this model with the other three models,these results show that the accuracy rate,recall rate and F1 value of this model are close to 90%,which are 2%,5%and 6%higher than those of the previous three models,respectively.[Conclusion]This study provides a more efficient method for automatic geological entity recognition for geological researchers in the Kaiyang phosphate mine.