Neural named entity recognition in Chinese electricity texts based on Bert-BiLSTM-CRF model

Shan Lin

doi:10.1117/12.2684577

19 October 2023 Neural named entity recognition in Chinese electricity texts based on Bert-BiLSTM-CRF model

Shan Lin

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270907 (2023) https://doi.org/10.1117/12.2684577
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the problems for electricity policy researchers to obtain policies related to specific entities, a deep learning model based on Bert-BiLSTM-CRF is proposed to address the problem by recognizing related entities. The model encodes the power policy text with characters through BERT, extracts features from the characters vectors through the BiLSTM network, and generates label sequences through CRF decoding. The experiment reveals that the BERT-BiLSTM-CRF model outperforms other standard models for the recognition of named entities in electricity texts, with the highest F1 score of 88.47%.

Citation Download Citation

Shan Lin "Neural named entity recognition in Chinese electricity texts based on Bert-BiLSTM-CRF model", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270907 (19 October 2023); https://doi.org/10.1117/12.2684577

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