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- W4312750086 abstract "The defect record of traction power supply equipment not only describes the defect situation of the equipment, but also provides historical experience for the defect treatment of similar equipment. The recognition of named entity information of the defect record is the premise of constructing the knowledge graph. In order to mine the entity information of defect records more efficiently and accurately, this paper proposes a named entity recognition algorithm to solve the current situation of lack of utilization and analysis of defect records of traction power supply equipment and low efficiency of manual information processing modes. The bidirectional encoder representation from transformers (BERT) pre-training language model is used as the encoding layer of the word vector, the bidirectional long short term memory (BiLSTM) is used as the character label prediction layer, and the conditional random field (CRF) is used to output the global optimal label. The defect records of the traction power supply equipment of a maintenance department from 2016 to 2019 are used as the data set for case study, and, the comprehensive evaluation index of entity recognition of the model has reached 94.66%, saving 98.50% time compared with manual recognition. The results show that the entity information recognition algorithm proposed in this paper can effectively and accurately mine the defect record information of traction power supply equipment." @default.
- W4312750086 created "2023-01-05" @default.
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- W4312750086 date "2022-07-08" @default.
- W4312750086 modified "2023-09-25" @default.
- W4312750086 title "Research on Efficient Information Mining Method for Defect Record of Traction Power Supply Equipment" @default.
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- W4312750086 doi "https://doi.org/10.1109/icpsasia55496.2022.9949792" @default.
- W4312750086 hasPublicationYear "2022" @default.
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