Syntactic Analysis of Power Grid Emergency Pre-plans Based on Transfer Learning

Abstract

To deal with the emergency pre-plans saved by the power grid dispatch department, so that the dispatcher can quickly retrieve and match similar accidents in the pre-plans, then they can learn from the experience of previous relevant situations, it is necessary to extract the information of the pre-plans and extract its key information. Therefore, deep learning method with strong generalization ability and learning ability and continuous improvement of model can be adopted. However, this method usually requires a large amount of data, but the existing labeling data in the power grid field is limited and the manual method for data labeling is a huge workload. Therefore, in the case of insufficient data, this paper aims to solve how to use deep learning method for effective information extraction? This paper modifies the ULMFiT model and uses it to carry out word vector training, adopting transfer learning method to introduce annotating datasets in the open field and combining with the data in the field of power grid to training model. In this way, the semantic relation of power grid domain is introduced into the syntactic analysis of the pre-plans, and we can further complete the information extraction. Experimental verification is carried out in this paper, the results show that, in the case of insufficient corpus or small amount of annotated data, this method can solve the problem of part of speech analysis errors, it can also improve the accuracy of syntactic analysis, and the experimental verifies the effectiveness of this method.