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):89-95.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 002]

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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: 89-95 Column: Public date: 2024-04-20

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

Author(s):: LIU Xin¹; XU Hongzhen¹; 2; LIU Aihua²; DENG Dejun¹; 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:: TP311

DOI:: 10. 13705/ j. issn. 1671-6833. 2024. 03. 002

Abstract:: The commonly used deep learning methods based on BERT pre-trained model in geological named entity recognition were character-based approaches, and could not utilize word-level information. Additionally, the dropout mechanism in neural networks might cause inconsistency between the training and inference stage. To address this issue, a geological named entity recognition model MBCR based on MacBERT and R-Drop was proposed. Firstly, MacBERT was used to learn text feature representations, which could fully utilize character and word information. Then, BiGRU was employed to encode context features, effectively extracting complete semantic information. Subsequently, CRF was adopted to capture dependencies between labels and generate the optimal label sequence. Moreover, R-Drop was introduced during the training process to further enhance the model′s generalization capabilities. Compared with BiLSTM-CRF, BERT-BiLSTM-CRF, and other models, the proposed MBCR model improved the F1-score on the NERdata dataset by 2. 08-4. 62 percentage points and on the Boson dataset by 1. 26-17. 54 percentage points.

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Last Update: 2024-04-29