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Tunnel construction safety domain named entity recognition based on BERT-BiLSTM-CRF
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Nian ZHANG1, 2, Caifeng ZHOU1, 2, Fei WAN3, Fei LIU1, Yaoyao WANG1, Dongliang XU1, 2
China Safety Science Journal | 2024, 34(12) : 56 - 63
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China Safety Science Journal | 2024, 34(12): 56-63
Safety engineering technology
Tunnel construction safety domain named entity recognition based on BERT-BiLSTM-CRF
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Nian ZHANG1, 2, Caifeng ZHOU1, 2, Fei WAN3, Fei LIU1, Yaoyao WANG1, Dongliang XU1, 2
Affiliations
  • 1 College of Civil Engineering,Taiyuan University of Technology,Taiyuan Shanxi 030024,China
  • 2 Research Center of Tunneling and Underground Engineering of Ministry of Education,Beijing Jiaotong University,Beijing 100044,China
  • 3 Research Institute of Highway Ministry of Transport,Beijing 100088,China
Published: 2024-12-28 doi: 10.16265/j.cnki.issn1003-3033.2024.12.0713
Outline
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To solve the problems existing in the traditional NER methods in the domain of tunnel construction safety,such as fuzzy entity boundary,difficulty in small-sample learning,and insufficiently comprehensive extraction of feature information,an entity recognition method for tunnel construction accident text based on the BERT-BiLSTM-CRF model was proposed. Firstly,the BERT model was used to encode the tunnel construction accident text to obtain word vectors containing semantic features. Then,the word vectors output after the training of the BERT model were input into the BiLSTM model to further obtain the context feature of the tunnel construction accident text and conduct label probability prediction. Finally,by utilizing the constraints of the annotation rules of the CRF layer,the output result of the BiLSTM model was corrected,and the maximum probability sequence annotation result was obtained,so as to realize the intelligent classification of the labels of the tunnel construction accident texts. Comparative experiments were conducted between this model and other four commonly used traditional NER models on the tunnel construction safety accident corpus dataset. The results show that the recognition accuracy rate,recall rate and F1 value of the BERT-BiLSTM-CRF model are 88%,89% and 88% respectively,and the entity recognition effect is better than other benchmark models. By using the established NER model to recognize the entities in the actual tunnel construction accident texts,its application effect in the domain of tunnel construction safety is verified.

bidirectional encoder representations from transformers (BERT)  /  bidirectional long short-term memory(BiLSTM)  /  conditional random fields (CRF)  /  tunnel construction  /  safety field  /  named entity recognition (NER)  /  deep learning
Nian ZHANG, Caifeng ZHOU, Fei WAN, Fei LIU, Yaoyao WANG, Dongliang XU. Tunnel construction safety domain named entity recognition based on BERT-BiLSTM-CRF[J]. China Safety Science Journal, 2024 , 34 (12) : 56 -63 . DOI: 10.16265/j.cnki.issn1003-3033.2024.12.0713
Year 2024 volume 34 Issue 12
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.12.0713
  • Receive Date:2024-09-14
  • Online Date:2025-07-09
  • Published:2024-12-28
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  • Received:2024-09-14
  • Revised:2024-11-18
Funding
Affiliations
    1 College of Civil Engineering,Taiyuan University of Technology,Taiyuan Shanxi 030024,China
    2 Research Center of Tunneling and Underground Engineering of Ministry of Education,Beijing Jiaotong University,Beijing 100044,China
    3 Research Institute of Highway Ministry of Transport,Beijing 100088,China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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