Research on Knowledge Extraction Method for High-speed Railway Signal Equipment Fault Based on Text

被引：0
|
作者：
Li X. [1 ,2 ]
Shi T. [2 ]
Li P. [1 ,2 ]
Dai M. [1 ,2 ]
Zhang X. [1 ,2 ]
机构：
[1] Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing
[2] China Academy of Railway Sciences Corporation Limited, Beijing
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2021年 / 43卷 / 03期
关键词：
BiLSTM+CRF; Knowledge extraction; Multidimensional character feature; Multidimensional word segmentation feature; Signal equipment fault;
D O I：
10.3969/j.issn.1001-8360.2021.03.012
中图分类号：
学科分类号：
摘要：
A pipeline knowledge extraction model of Named Entity and Entity Relationship was proposed for the fault text data of high-speed railway signal equipment. The model implemented fault knowledge extraction of signal equipment by uniform labeling and training Named Entity Recognition and Entity Relation Extraction respectively. The knowledge structure and sample labeling method of signal equipment fault were defined, and a Named Entity feature representation method based on multi-dimensional character feature representation was proposed. In addition, BiLSTM+CRF was adopted to realize the Named Entity Recognition, and the representation method of entity relations based on multi-dimensional word segmentation features was proposed. Furthermore, the transformer network was designed to realize the Entity Relation Extraction based on multi-dimensional word segmentation features. The experimental results from the experimental analysis on the 10-year fault data of signal switch machine of high-speed railway show that the Named Entity and Relation Extraction model for high-speed railway signal equipment fault has high evaluation index and can be applied to text-based fault knowledge extraction. © 2021, Department of Journal of the China Railway Society. All right reserved.
引用
收藏
页码：92 / 100
页数：8
相关论文
共 18 条
  • [1] (2018)
  • [2] ZHANG Dianye, JIN Jian, YANG Jingshuai, Theoretic and Technical Research Framework of Railway Transportation Safety, China Railway Science, 26, 3, pp. 114-118, (2005)
  • [3] (2019)
  • [4] QIN Y, SHEN G W, ZHAO W B, Et al., A Network Security Entity Recognition Method based on Feature Template and CNN-BiLSTM-CRF, Frontiers of Information Technology & Electronic Engineering, 20, 6, pp. 872-884, (2019)
  • [5] (2018)
  • [6] LI X Q, SHI T Y, LI P, Et al., BiLSTM-CRF Model for Named Entity Recognition in Railway Accident and Fault Analysis Report, 7th International Conference on Transportation and Traffic Engineering, pp. 1-5, (2018)
  • [7] ZHANG Xiaoming, WANG Zhijun, LIANG Liping, A Stacking Algorithm for Convolution Neural Network, Computer Engineering, 44, 4, pp. 243-247, (2018)
  • [8] WANG C L, JIANG F J, YANG H X., A Hybrid Framework for Text Modeling with Convolutional RNN, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2061-2069, (2017)
  • [9] LEI T, BARZILAY R, JAAKKOLA T., Molding CNNs for Text: Non-linear, Non-consecutive Convolutions, Indiana University Mathematics Journal, 58, 3, pp. 1151-1186, (2015)
  • [10] WANG Wei, SUN Yuxia, QI Qingjie, Et al., Text Sentiment Classification Model based on BiGRU-attention Neural Network, Application Research of Computers, 36, 12, pp. 3558-3564, (2019)
12