Electric Power Audit Text Classification With Multi-Grained Pre-Trained Language Model-Reference-Cited by-同舟云学术

Electric Power Audit Text Classification With Multi-Grained Pre-Trained Language Model

Author:

Meng Qinglin¹^ORCID,Song Yan¹,Mu Jian²,Lv Yuanxu³,Yang Jiachen⁴^ORCID,Xu Liang⁵,Zhao Jin⁶,Ma Junwei⁷,Yao Wei⁸,Wang Rui⁹,Xiao Maoxiang¹⁰,Meng Qingyu¹¹

Affiliation:

1. Comprehensive Service Center, State Grid Tianjin Electric Power Company, Tianjin, China

2. State Grid Tianjin Electric Power Company, Tianjin, China

3. State Grid Corporation of China, Beijing, China

4. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

5. Tianjin Tianyuan Power Engineering Company Ltd., Baodi Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin, China

6. Electric Power Research Institute, State Grid Shanxi Electric Power Company, Taiyuan, China

7. Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan, China

8. Taiyuan Power Supply Company, State Grid Shanxi Electric Power Company, Shanxi, Taiyuan, China

9. Chengxi Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin, China

10. Ningdongshengyuan Electric Power Engineering Company Ltd., Ninghe Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin, China

11. Zhangjiakou Wanquan District Power Supply Branch, State Grid Jibei Electric Power Company Ltd., Heibei, Zhangjiakou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference29 articles.

1. BERT: Pre-training of deep bidirectional transformers for language understanding;kenton;Proc NAACL-HLT,2019

5. Power equipment defect record text mining based on BiLSTM-attention neural network;feng;Proc CSEE,2020

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