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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (6): 1186-1194    DOI: 10.3785/j.issn.1008-973X.2023.06.014
    
Continual learning framework of named entity recognition in aviation assembly domain
Pei-feng LIU(),Lu QIAN,Xing-wei ZHAO*(),Bo TAO
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract  

In order to build an aviation assembly knowledge graph composed of assembly process information, assembly technology knowledge, related industry standards and internal connections of the three, a named entity recognition technology framework based on continual learning was proposed. The characteristic of the proposed framework was that it maintained high recognition performance throughout the progressive learning process from zero corpus to large-scale corpus, without relying on manual feature setting. A comparative performance experiment of the proposed framework was carried out in practical industrial scenarios, the experiment proceeded from general assembly and component assembly, and the manipulations of the pull rod and cable installation were regard as a specific experimental case. Experimental results show that the proposed framework is significantly better in accuracy, recall, and F1 value than previous algorithms, while handling different-scale corpus environments. And the credible results for named entity recognition tasks can be provided consistently by the proposed framework in the aviation assembly domain.

Key words： intelligent manufacturing      aviation assembly      named entity recognition      continual learning      deep learning     
Received: 14 June 2022      Published: 30 June 2023
CLC:  TU 111  
Fund:  国家自然科学基金资助项目（52275020, 62293514）
Corresponding Authors: Xing-wei ZHAO     E-mail: stevenpliu@hust.edu.cn;zhaoxingwei@hust.edu.cn
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Pei-feng LIU
Lu QIAN
Xing-wei ZHAO
Bo TAO
Cite this article:

Pei-feng LIU,Lu QIAN,Xing-wei ZHAO,Bo TAO. Continual learning framework of named entity recognition in aviation assembly domain. Journal of ZheJiang University (Engineering Science), 2023, 57(6): 1186-1194.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.06.014     OR     https://www.zjujournals.com/eng/Y2023/V57/I6/1186


航空装配领域中命名实体识别的持续学习框架

为了构建航空装配领域中装配流程信息、装配技术知识、行业标准和三者内在联系组成的航空装配知识图谱，提出基于持续学习的命名实体识别技术框架. 所提框架的特点是从零语料到大规模语料的渐进式学习过程中，在不依赖人工设定特征的情况下，始终保持较高的识别效果. 从飞机总装配和部件对接的实际工业场景展开所提框架的性能对比实验，并以操纵拉杆和钢索的安装为实验案例. 实验结果表明，在处理不同规模的语料环境的情况下，所提框架在正确率、召回率、F1值上均显著优于以往算法，所提框架可以为航空装配领域命名实体识别任务持续提供可信的结果.


关键词： 智能制造,  航空装配,  命名实体识别,  持续学习,  深度学习 
模型 P/% R/% F1/%
CRF（2006）[16] 91.22 81.71 86.20
CRF（2013）[17] 91.86 88.75 90.28
BiLSTM+CRF[18] 92.97 90.80 91.87
Lattice LSTM[18] 93.57 92.79 93.18
BERT-BiGRU-CRF[19] 95.31 95.54 95.43
Tab.1 Experimental results of named entity recognition models on MSRA corpus
航空装配本体 现有知识库本体
组件实体（component） 产品实体（product）
固定设施实体（facility） 固定平台实体（plant）
操作项目实体（operation） 工序实体（process）
工序步骤实体（step） 工序计划实体（process plan）
工具实体（tool） 工具实体（tool）
Tab.2 Comparison table of aviation assembly ontology and existing knowledge ontology
Fig.1 Example of component entities in aviation assembly domain
Fig.2 Human-computer interaction tool interface
实体类别 nce
AA-1 AA-2 总计
组件实体（component） 336 213 549
固定设施实体（facility） 92 3 95
操作项目实体（operation） 263 91 354
工序步骤实体（step） 122 6 128
工具实体（tool） 139 18 157
Tab.3 Statistics of corpus entity count
Fig.3 Continual learning framework for named entity recognition
Fig.4 Basic structure of long-short term memory unit
Fig.5 Basic structure of character-based bidirectional long-short term memory with conditional random field model
语料库 n 标注方式
训练集 验证集 测试集
MSRA 1 921 489 246 370 229 910 BIO
AA-1 9 364 621 665 BMEO
AA-2 13 068 709 653 BMEO
Tab.4 Corpus information statistics
n F1/%
HMM CRF BiLSTM BiLSTM+CRF 本研究
5 468 89.20 87.71 87.25 87.24 89.20
10 437 89.49 88.20 87.33 87.31 89.49
20 201 90.44 89.05 87.49 88.80 90.44
40 061 91.44 90.63 90.66 90.59 91.66
80 095 92.74 92.79 91.28 92.50 93.05
1 921 489 94.82 98.05 97.51 98.02 98.21
Tab.5 F1-score of different models on MSRA corpus
Fig.6 Variation curves of F1 values and training word counts on MSRA corpus
模型 P/% R/% F1/%
HMM 94.76 94.90 94.82
CRF 98.03 98.07 98.05
BiLSTM 97.49 97.54 97.51
BiLSTM+CRF 98.01 98.04 98.02
本研究 98.19 98.23 98.21
Tab.6 Experimental results of different models on MSRAcorpus
实体类别 nce,t
AA-1 AA-2
组件实体（component） 27 42
固定设施实体（facility） 6 —
操作项目实体（operation） 19 29
工序步骤实体（step） 10 —
工具实体（tool） 2 4
Tab.7 Number of entities in test set for two aviation assembly corpuses
模型 P/% R/% F1/%
HMM 89.94 86.62 88.24
CRF 86.33 85.85 86.09
BiLSTM 76.35 78.46 77.39
BiLSTM+CRF 74.44 78.15 76.25
本研究 89.94 86.62 88.24
Tab.8 Experimental results of different models on AA-1 corpus
实体类别 P/% R/% F1/%
组件实体（component） 75.86 81.48 78.57
固定设施实体（facility） 100 33.33 50.00
操作项目实体（operation） 69.23 94.74 80.00
工序步骤实体（step） 75.00 90.00 81.82
工具实体（tool） 12.50 50.00 20.00
Tab.9 Experimental results of continual learning framework on five entities from AA-1 corpus
模型 P/% R/% F1/%
HMM 85.12 86.39 85.75
CRF 84.03 85.60 84.81
BiLSTM 79.98 82.75 81.34
BiLSTM+CRF 79.61 81.80 80.69
本研究 85.12 86.39 85.75
Tab.10 Experimental results of different models on AA-2 corpus
实体类别 P/% R/% F1/%
组件实体（component） 83.33 71.43 76.77
操作项目实体（operation） 79.31 79.31 79.31
工具实体（tool） 66.67 50.00 57.14
Tab.11 Experimental results of continual learning framework on three entities from AA-2 corpus
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