[1]刘 昕,徐洪珍,刘爱华,等.基于MacBERT 和R-Drop 的地质命名实体识别[J].郑州大学学报(工学版),2024,45(03):89-95.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 002]
 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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基于MacBERT 和R-Drop 的地质命名实体识别()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年03期
页码:
89-95
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Geological Named Entity Recognition Based on MacBERT and R-Drop
文章编号:
1671-6833( 2024) 03-0089-07
作者:
刘 昕1 徐洪珍12 刘爱华2 邓德军1
1. 东华理工大学 信息工程学院,江西 南昌 330013;2. 东华理工大学 软件学院,江西 南昌 330013
Author(s):
LIU Xin1 XU Hongzhen12 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
关键词:
命名实体识别 地质 MacBERT BiGRU R-Drop
Keywords:
named entity recognition geology MacBERT BiGRU R-Drop
分类号:
TP311
DOI:
10. 13705/ j. issn. 1671-6833. 2024. 03. 002
文献标志码:
A
摘要:
地质命名实体识别中常用的基于BERT 预训练模型的深度学习方法是基于字的方法,没有利用词信息,且神经网络中的Dropout 机制会导致训练阶段和推理阶段之间存在不一致性。针对该问题, 提出了一种基于MacBERT 和R-Drop 的地质命名实体识别模型MBCR。首先,通过MacBERT 学习文本特征表示,充分利用字词信息;其次,运用BiGRU 编码上下文特征,有效提取完整的语义信息;最后,采用CRF 获取标签间的依赖关系,生成最优标签序列。此外,在训练过程中引入R-Drop,进一步提升模型的泛化能力。结果表明:与BiLSTM-CRF、BERTBiLSTM-CRF 等模型相比,所提MBCR 模型在NERdata 数据集上的F1 值提高了2. 08百分点~4. 62百分点,在Boson数据集上的F1 值提高了1. 26百分点~17. 54百分点。
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2023-09-15;修订日期:2023-10-20
基金项目:国家自然科学基金资助项目(62066003);江西省教育厅科技计划项目(GJJ160554);江西省抚州市人才计划项目(2021ED008);江西省网络空间安全智能感知重点实验室室开放项目(JKLCIP202202)
通信作者:徐洪珍(1976—),男,江西抚州人,东华理工大学教授,博士,主要从事机器学习、大数据、云计算研究,E-mail:xuhz@ ecut. edu. cn。
更新日期/Last Update: 2024-04-29