Geological Named Entity Recognition Based on MacBERT and R-Drop

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Geological Named Entity Recognition Based on MacBERT and R-Drop

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[1]LIU Xin,XU Hongzhen,LIU Aihua,et al.Geological Named Entity Recognition Based on MacBERT and R-Drop[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):38-45.[doi:10. 13705 / j. issn. 1671-6833. 2023. 06. 009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 38-45 Column: Public date: 2024-04-20

Title:: Geological Named Entity Recognition Based on MacBERT and R-Drop

Author(s):: LIU Xin¹; XU Hongzhen ¹; 2; LIU Aihua ²; DENG Dejun ¹; 1. School of Information Engineering, East China University of Technology, Nanchang 330013, China; 2. School of Software, East China University of Technology, Nanchang 330013, China

Keywords:: named entity recognition; geology; MacBERT; BiGRU; R-Drop

CLC:: TP391

DOI:: 10. 13705 / j. issn. 1671-6833. 2023. 06. 009

Abstract:: To tackle the problem of low accuracy of detection and recognition for object in complex scenes, YOLOv5 object detection and recognition algorithm based on attention and multistage feature fusion(AMFF) was proposed in this study. The main ideas included adding the proposed dual space directions pyramid split attention (DSD-PSA) mechanism to the backbone network of the traditional YOLOv5s model to enhance the learning of the feature map space and channel information, adopting multistage feature fusion(MFF) structure in the bottleneck network to fuse the features of different branches, increasing richness of the feature and improving the ability to cope with complex scenes. In addition, C3Ghost module and depthwise separable convolution were used to replace C3 module and common convolution to reduce the number of parameters and the complexity of network. Compared with the traditional YOLOv5s algorithm, the mean average accuracy of the proposed algorithm in the VOC2007+2012 data set reached 85%, and the mean average accuracy of the smart retail cabinet commodity identification data set reached 97.2%, which verified the effectiveness and feasibility of the proposed algorithm.

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Last Update: 2024-04-29