Bi-LSTM-CRF-based named entity recognition for power administration
Chenying Feng, Xiaodong Xu, Runheng Tang, Shengwei Shi, Liang Chen, Miao Yu, xirui guo · 2023
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.