Research on text relation extraction of power administrative duty based on improved pre-trained language model

Strategies for efficiently and accurately extracting relationships from power system administrative shift texts are explored using an optimized pre-trained language model, such as BERT (Bidirectional Encoder Representations from Transformers). Tailored improvements to the model address the nuanced and variable nature of power texts, including the integration of domain-specific pre-training corpora and architectural enhancements. The model, enhanced with the R-BERT (Relation-Bidirectional Encoder Representations from Transformers) algorithm, demonstrates advanced proficiency in identifying entity positions and yields exceptional results in experimental evaluations, particularly achieving a high F1 score for the identification of cooperative relationships. The findings indicate that this innovation can significantly enhance the level of intelligence in power system administrative shift management. Subsequent developments may involve the incorporation of additional expert knowledge to further refine the model.