Bi-LSTM-CRF-based named entity recognition for power administration

Chenying Feng; Xiaodong Xu; Runheng Tang; Shengwei Shi; Liang Chen; Miao Yu; Xirui Guo

doi:10.1117/12.2681609

1 June 2023 Bi-LSTM-CRF-based named entity recognition for power administration

Chenying Feng, Xiaodong Xu, Runheng Tang, Shengwei Shi, Liang Chen, Miao Yu, Xirui Guo

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127182D (2023) https://doi.org/10.1117/12.2681609
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

A large number of semi-structured and unstructured texts have been accumulated during the operation of power administration. Scientific mining of the value behind these texts plays a key role in the digital transformation of power grid companies. In this paper, we adopt Bi-LSTM-CRF model for power administrative entity recognition, firstly classify and label the power administrative texts, and then adopt the above mentioned model for power administrative entity recognition, and compare it with HMM, CRF and BiLSTM models experimentally. The final experimental results prove that the Bi-LSTM-CRF model entity recognition has the highest accuracy rate and is more effective in identifying entities in electric power administration.

Citation Download Citation

Chenying Feng, Xiaodong Xu, Runheng Tang, Shengwei Shi, Liang Chen, Miao Yu, and Xirui Guo "Bi-LSTM-CRF-based named entity recognition for power administration", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127182D (1 June 2023); https://doi.org/10.1117/12.2681609

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KEYWORDS

Data modeling

Power grids

Statistical modeling

Education and training

Deep learning

Neural networks

Data storage

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