Entity Recognition Method for Power Data Based on Enhanced Optimization Pre-trained Language Model
Knowledge graph can effectively integrate multi-source data in the power system,improve the level of grid knowledge management.In light of the scarcity of power datasets,diverse entity types and strong professionalism,a method for power data entity recognition based on enhanced optimization pre-trained language model is proposed.This method utilizes data augmentation techniques based on entity word bags to expand the original dataset,employs enhanced optimization pre-trained language model for dynamic semantic encoding,and utilizes bidirectional long short term memory networks and conditional random fields to extract features and optimize labels.Experimental results demonstrate that this entity recognition method outperforms traditional deep learning-based entity recognition methods by 2.17%in F1 score,its effectiveness is confirmed in constructing knowledge graphs for power data.
knowledge graphentity recognitiondata augmentationpre-trained language modelbidirectional long short term memory networkconditional random field
万方数据
维普
