基于大模型的大规模电力数据零样本实体关系抽取方法OA

In the era of big data,the extraction of entity relations triples from data in the electric power field is of great signifi-cance to the structuring of electric power data.However,traditional deep learning methods require a large amount of labeled training data in the power field for deep learning model learning.This method is undoubtedly time-consuming and labor-intensive,and cannot achieve good entity relations extraction results.In response to these problems,this pa-per proposes a zero-shot entity relations extraction method in the electric power field called chat relation exaction(ChatRE)based on a large language model.The two-stage multi-round dialogue method proposed by ChatRE divides the entity relations extrac-tion into two sub-tasks and uses syntactic enhancement.This further improves the extraction effect and effectively solves the above problems.This paper conducts an experimental evaluation of ChatRE based on the constructed elec-tric power field entity data set and general data set.The results show that the ChatRE model proposed in this paper is better than existing methods and can effectively extract entity relations triples from complex power domain knowledge.