[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 minerali-zation in massive documents is time-consuming and inefficient.It cannot meet the needs of knowledge sharing,dis-semination and intelligent management of geological reports.[Methods]To quickly obtain the ore-forming geolog-ical 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 la-belling 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(bidirec-tional 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-form-ing 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 au-tomatic geological entity recognition for geological researchers in the Kaiyang phosphate mine.
geological entity recognition/extreme learning machine network(XLNET)-bidirectional long short term memory(BILSTM)-self attention layer(Attention)-conditional random field(CRF)/metallogenic model of phosphate ore/pre-training model/sequence labeling
维普
万方数据