Entity Recognition of Power Fault Disposal based on Attention Model
Yupeng He, Shaohua Zhang, Jinpeng Zhang, Renhe Zhang, Lin Zhu · 2022
Power grid fault disposal preplan is an important reference for power grid fault disposal. Hence, extracting finegrained key entity information such as power equipments, name and number from the preplan is an important basis for the computer to understand the content and further support the intelligent disposal. A named entity recognition technology for power grid fault disposal preplan is proposed based on deep learning. Firstly, the character vector is used to represent the preplan text. Then the character features are extracted by combining the attention mechanism and the bidirectional long short-term memory network. Finally, the optimal serialization annotation is solved by the conditional random field. The example shows that the proposed entity recognition model can automatically and efficiently extract text features, thus accurately identifying entity words in the preplan. It proves that the model has better robustness.