The entity and event recognition method of power dispatching text information based on BERT-CRF

Abstract

In order to improve the accuracy of power dispatching text analysis and the ability to guide the operation of the power grid, a power dispatch text entity recognition method is proposed based on Bidirectional Encoder Representations from Transformers-Conditional Random Field (BERT-CRF). Taking the power grid fault handling plan text as the research object, the entity marking method of the fault handling plan is proposed. The word vector of the plan entity is calculated based on the BERT pre-training model, the characterization ability of the professional entity of the plan is enhanced by fine-tuning the initial BERT parameters, and the recognition ability of the plan text sequence is improved from the overall situation to access the CRF layer in the neural network. Thus, an entity recognition model of fault handling plan is established based on the BERT-CRF. Through the verification of a power grid fault handling plan, the proposed method has higher power dispatch entity and event recognition accuracy compared with other algorithms.