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    基于BERT-BiGRU-CRF模型的岩土工程实体识别

    王权于 李振华 涂志鹏 陈冠宇 胡君 陈嘉麒 陈建军 吕国斌

    王权于, 李振华, 涂志鹏, 陈冠宇, 胡君, 陈嘉麒, 陈建军, 吕国斌, 2023. 基于BERT-BiGRU-CRF模型的岩土工程实体识别. 地球科学, 48(8): 3137-3150. doi: 10.3799/dqkx.2022.462
    引用本文: 王权于, 李振华, 涂志鹏, 陈冠宇, 胡君, 陈嘉麒, 陈建军, 吕国斌, 2023. 基于BERT-BiGRU-CRF模型的岩土工程实体识别. 地球科学, 48(8): 3137-3150. doi: 10.3799/dqkx.2022.462
    Wang Quanyu, Li Zhenhua, Tu Zhipeng, Chen Guanyu, Hu Jun, Chen Jiaqi, Chen Jianjun, Lv Guobin, 2023. Geotechnical Named Entity Recognition Based on BERT-BiGRU-CRF Model. Earth Science, 48(8): 3137-3150. doi: 10.3799/dqkx.2022.462
    Citation: Wang Quanyu, Li Zhenhua, Tu Zhipeng, Chen Guanyu, Hu Jun, Chen Jiaqi, Chen Jianjun, Lv Guobin, 2023. Geotechnical Named Entity Recognition Based on BERT-BiGRU-CRF Model. Earth Science, 48(8): 3137-3150. doi: 10.3799/dqkx.2022.462

    基于BERT-BiGRU-CRF模型的岩土工程实体识别

    doi: 10.3799/dqkx.2022.462
    基金项目: 

    认知智能全国重点实验室开放课题 COGOS-2023HE09

    国家自然科学基金的基金 42103024

    国家自然科学基金的基金 42130307

    详细信息
      作者简介:

      王权于(1970-),男,研究员,主要从事大数据、高性能计算及智能地学方面研究工作,ORCID:0000-0003-2338-4855,E-mail:wangqy@cug.edu.cn

      通讯作者:

      吕国斌, ORCID:0009-0003-6460-4145. E-mail: lvgb@cug.edu.cn

    • 中图分类号: P642

    Geotechnical Named Entity Recognition Based on BERT-BiGRU-CRF Model

    • 摘要: 岩土工程实体识别是岩土工程文本挖掘和知识谱图的工作基础和重要前提. 针对岩土工程实体识别问题,参考《GB/T 50279-2014:岩土工程基本术语标准》等国家行业标准规范,设计和构建了一个小规模的岩土工程命名实体语料库;提出了一种岩土工程文本命名实体识别深度学习模型BERT-BiGRU-CRF(简称:GENER):表示学习层采用BERT预训练语言模型实现了岩土工程文本特征的迁移表示学习;BiGRU上下文编码层实现对岩土工程文本上下文特征编码;CRF标签解码层解决了标签间依赖约束,生成符合标注规律的岩土工程命名实体标签序列;最后,基于岩土工程命名实体语料库,对GENER模型进行了实验分析. 在对照实验中,取得了良好效果:精确率P达到了90.94%,召回率R达到了92.88%,F1值达到了91.89%,模型训练速度提升了4.735%. 实验结果表明相比基线模型BiLSTM-CRF和其他预训练模型,GENER模型在小规模语料岩土工程命名实体识别方面效果更优,未来可推广应用到其他地质类文本命名实体识别任务.

       

    • 图  1  岩土工程命名实体语料库构建过程

      Fig.  1.  Constructing process for the geotechnical named entity corpus

      图  2  岩土工程文本语料实例

      Fig.  2.  Examples for Geotechnical corpus

      图  3  YEDDA命名实体标注环境

      Fig.  3.  Named entity labels with YEDDA

      图  4  GENER模型结构图

      Fig.  4.  Structure of the GENER model

      图  5  表示学习层预训练模型结构

      Fig.  5.  Structure of the pretrained language model in the representation learning layer

      图  6  GRU细胞单元结构

      Fig.  6.  Structure of the GRU cell unit

      图  7  上下文编码层模型结构

      Fig.  7.  Structure of the context coding layer model

      图  8  标签解码层模型结构

      Fig.  8.  Structure of the label decoding layer model

      图  9  精确率随迭代次数变化图

      Fig.  9.  Changes of the precision along with epochs

      图  10  召回率随迭代次数变化图

      Fig.  10.  Changes of the recall along with epochs

      图  11  F1值随迭代次数变化图

      Fig.  11.  Changes of the F1 score along with epochs

      图  12  精确率随批大小变化图

      Fig.  12.  Changes of the precision along with batch size

      图  13  召回率随批大小变化图

      Fig.  13.  Changes of the recall along with batch size

      图  14  F1值随批大小变化图

      Fig.  14.  Changes of the F1 score along with batch size

      表  1  岩土工程命名实体类型

      Table  1.   Types of geotechnical named entities

      实体类型 简称 说明
      岩土工程名称 GEN 切坡、边坡、高切坡等岩土工程名称
      岩土工程地质 GEO 岩土工程物质组成和地质结构等
      工程勘测方法 SUR 工程测量、工程地质测绘、勘探、试验等岩土工程勘察方法
      工程属性参数 FEA 形态特征、地质结构、岩土物理力学参数等属性参数
      工程评测方法 EVA 变形破坏模式、预测变形破坏模式等
      工程防护方法 DEF 岩土工程防护方案和措施
      下载: 导出CSV

      表  2  岩土工程命名实体标注实例

      Table  2.   Examples for geotechnical named entity labels

      实体示例 标注示例
      (1)秭归县泄滩乡石门高切坡位于长江北岸坊家山村一组石门,距秭归新县城约70 km (1)秭/B_GEN归/M_GEN县/M_GEN泄/M_GEN滩/M_GEN乡/M_GEN石/M_GEN门/M_GEN高/M_GEN切/M_GEN坡/E_GEN位/O于/O长/O江/O北/O岸/O坊/O家/O山/O村/O-/O组/O石/O门/O,/O距/O秭/O归/O新/O县/O城/O约/O7/O0/OK/Om/O. /O
      (2)高切坡的物质组成主要为前震旦系(γ1)闪云斜长花岗岩,局部鲜见伟晶岩脉发育 (2)高/O切/O坡/O的/O物/O质/O组/O成/O主/O要/O为/O闪/B_GEO云/M_GEO斜/M_GEO长/M_GEO花/M_GEO岗/M_GEO岩/E_GEO,/O局/O部/O鲜/O见/O伟/B_GEO晶/M_GEO岩/M_GEO脉/E_GEO发/O育/O. /O
      (3)该段高切坡坡顶高程201~214 m,坡脚高程181.1 m,坡度50°~57°,高20~33 m (3)该/O段/O高/O切/O坡/O坡/B_FEA顶/M_FEA高/M_FEA程/E_FEA2/O0/O1/Om/O~/O2/O1/O4/Om/O,/O坡/B_FEA脚/M_FEA高/M_FEA程/E_FEA1/O8/O1/O./O1/Om/O,/O坡/B_FEA度/E_FEA5/O0/O°/O~/O5/O7/O°/O,/O坡/B_FEA高/E_FEA2/O0/O~/O3/O3/Om/O. /O
      (4)制定了以工程地质测绘为主,辅以少量山地和钻探工作的勘察方案 (4)制/O定/O了/O以/O工/B_SUR程/M_SUR地/M_SUR质/M_SUR测/M_SUR绘/E_SUR为/O主/O,/O辅/O以/O少/O量/O山/O地/O和/O钻/B_SUR探/E_SUR工/O作/O的/O勘/O察/O方/O案/O. /O
      (5)切坡的变形破坏主要表现为坡面在雨水冲蚀作用下形成的水砂流冲蚀破坏. 该段切坡的变形破坏模式为局部破坏 (5)切/O坡/O的/O变/O形/O破/O坏/O主/O要/O表/O现/O为/O坡/O面/O在/O雨/O水/O冲/O蚀/O作/O用/O下/O形/O成/O的/O水/B_EVAL砂/M_EVAL流/M_EVAL冲/M_EVAL蚀/M_EVAL破/M_EVAL坏/E_EVAL, /O该/O段/O切/O坡/O的/O变/O形/O破/O坏/O模/O式/O为/O局/B_EVAL部/M_EVAL破/M_EVAL坏/E_EVAL. /O
      (6)坡顶后缘设计地表截水沟,坡底设计排水沟,坡面设深排水孔等,以减少地表水入渗 (6)坡/O顶/O后/O缘/O设/O计/O地/B_DEF表/M_DEF截/M_DEF水/M_DEF沟/E_DEF,/O坡/O底/O设/O计/O排/B_DEF水/M_DEF沟/E-DEF,/O坡/O面/O设/O深/B_DEF排/B_DEF水/M_DEF孔/E-DEF等/O,/O以/O减/O少/O地/O表/O水/O入/O渗/O. /O
      下载: 导出CSV

      表  3  实验设置信息

      Table  3.   Experimental settings

      环境 工具 详情
      硬件 CPU Intel(R) Xeon(R) Gold 6248
      GPU Tesla V100S-PCIE-32GB
      内存 512GB
      软件 操作系统 Centos7
      开发语言 Python 3.7.2
      深度学习框架 Pytorch 1.6.0
      下载: 导出CSV

      表  4  GENER模型超参数配置

      Table  4.   Hyperparameter configurations of the GENER model

      参数名称 参数值
      Max_Sentence_Length 128
      预训练层 子嵌入向量维度 768
      编码器堆叠数 内存
      自注意力头数 12
      上下文编码层 是否双向(Directional) True
      网络层数 2
      上下文特征编码维度 384
      偏置(Bias) True
      Batch_First True
      Dropout 0.1
      Num_workers 8
      下载: 导出CSV

      表  5  数据集命名实体统计分布

      Table  5.   Statistical distribution of named entities in the datasets

      训练集 验证集 测试集
      岩土工程名称(GEN) 3 312 419 472
      岩土工程地质(GEO) 10 304 1 294 1 431
      工程属性参数(FEA) 12 202 1 465 1 519
      工程勘测方法(SUR) 2 710 333 396
      工程评测方法(EVA) 4 344 536 591
      工程防护方法(DEF) 4 078 522 540
      总计 36 950 4 569 4 949
      下载: 导出CSV

      表  6  学习率与精确率P、召回率RF1值关系表

      Table  6.   Relationships between learning rate and precision, recall, and F1 score

      模型/学习率 le−1 5e−1 le−2 5e-2 le−3 5e−3 le-4 5e−4 le−5 5e−5 le−6 5e−6
      P 74.75 29.04 80.69 76.99 85.82 84.57 83.41 85.31 67.70 91.46 5.72 62.61
      BiLSTM-CRF R 74.75 29.04 80.54 76.55 85.82 84.44 83.41 84.98 67.70 89.90 5.67 62.61
      F1 74.75 29.04 80.61 76.77 85.82 84.51 83.41 85.14 67.70 90.66 5.70 62.61
      P 71.29 69.22 79.81 71.27 84.70 84.50 84.56 84.37 74.88 90.79 21.08 61.49
      ERNIE-BiLSTM-CRF R 70.96 68.93 79.81 70.42 84.64 82.53 84.56 84.37 73.33 92.53 20.19 61.49
      F1 71.12 69.07 79.81 70.84 84.67 83.50 84.56 84.37 74.10 91.64 20.63 61.49
      P 71.72 14.93 88.51 71.72 91.11 88.93 86.05 88.51 80.75 90.12 71.86 71.02
      BERT-BiLSTM-CRF R 71.72 0.38 88.51 71.72 91.11 88.93 86.05 88.51 80.38 91.81 69.66 70.88
      F1 71.72 0.75 88.51 71.72 91.11 88.93 86.05 88.51 80.57 90.94 70.74 70.95
      P 28.57 11.80 11.34 71.72 79.20 11.80 84.90 84.67 72.34 85.86 70.79 71.48
      RoBERTa-BiLSTM-CRF R 84.29 11.80 11.34 71.72 79.20 11.80 84.90 84.67 72.34 91.32 70.46 70.96
      F1 1.64 11.80 11.34 71.72 79.20 11.80 84.90 84.67 72.34 88.47 70.62 71.22
      P 71.72 63.66 76.21 68.66 88.03 84.83 87.14 87.49 83.50 88.17 77.55 79.39
      XLNET-BiLSTM-CRF R 71.72 83.44 75.86 54.98 87.93 84.83 86.97 87.32 81.46 91.47 36.93 72.91
      F1 71.72 71.28 76.04 61.06 87.98 84.83 87.06 87.40 82.47 89.77 50.04 76.01
      P 29.62 30.69 90.08 75.54 90.84 89.77 89.96 90.34 83.52 90.94 70.98 75.58
      GENER R 29.62 30.69 90.08 71.34 90.84 89.77 89.96 90.34 83.52 92.88 70.00 75.52
      F1 29.62 30.69 90.08 73.38 90.84 89.77 89.96 90.34 83.52 91.89 70.49 75.55
      下载: 导出CSV

      表  7  消融实验结果对比

      Table  7.   Results of the ablation experiment

      Model P R F1 s/epoch
      BiGRU-CRF 91.66 91.29 91.46 1 118
      BERT-CRF 90.88 90.69 90.78 9 564
      GENER 91.25 93.01 92.11 11 321
      下载: 导出CSV

      表  8  最优参数设置

      Table  8.   Optimal parameter settings

      模型名称 epoch 批大小 学习率
      BiLSTM-CRF 100 10 5e-5
      ERNIE-BiLSTM-CRF 100 10 5e-5
      BERT-BiLSTM-CRF 100 20 5e-5
      RoBERTa-BiLSTM-CRF 100 20 5e-5
      XLNET-BiLSTM-CRF 100 10 5e-5
      GENER 100 20 5e-5
      下载: 导出CSV

      表  9  最优参数下不同模型性能数据

      Table  9.   Performance of different models under optimal parameter settings

      Model P R F1 s/epoch
      BiLSTM-CRF 91.46 89.90 90.66 1 420
      ERNIE-BiLSTM-CRF 90.79 92.53 91.64 11 300
      BERT-BiLSTM-CRF 89.97 92.31 91.10 11 857
      RoBERTa-BiLSTM-CRF 87.57 91.79 89.61 11 806
      XLNET-BiLSTM-CRF 88.17 91.47 89.77 36 562
      GENER 91.25 93.01 92.11 11 321
      下载: 导出CSV

      表  10  GENER模型各命名实体分类精度

      Table  10.   The classification accuracy of each named entity in GENER model

      P R F1 Support
      岩土工程名称(GEN) 91.61 98.05 94.72 472
      岩土工程地质(GEO) 93.89 95.05 94.47 1 431
      工程属性参数(FEA) 93.36 93.16 93.26 1 519
      工程勘测方法(SUR) 89.39 88.59 88.99 396
      工程评测方法(EVA) 80.54 85.26 82.83 591
      工程防护方法(DEF) 91.08 94.12 92.58 540
      总计 91.25 93.01 92.11 4 949
      下载: 导出CSV
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    • 收稿日期:  2022-12-01
    • 刊出日期:  2023-08-25

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