Power text information extraction based on multi-task learning

In order to improve the analysis and processing speed of power system fault text in actual business scenarios, a power fault text information extraction model based on pre-training and multi-task learning was proposed. The pre-training model was used to learn the context information of power text words. The first-order and second-order fusion features of words were mined, which enhanced the representation ability of features. The multi-task learning framework was used to combine named entity recognition and relation extraction, which realized the mutual supplement and mutual promotion of entity recognition and relationship extraction, so as to improve the performance of power fault text information extraction. The model was verified by the daily business data of a power data center. Compared with other models, the proposed model’s accuracy and recall of power fault text entity recognition and relation extraction were improved. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.