MusREL

Abstract

In order to enhance the utility of online educational digital resources, the authors propose a practical and efficient multi-strategy relation extraction (RE) model in online education scenarios. First, the effective relation discrimination model is used to make relation predictions for non-structured teaching resources and eliminate the noise data. Then, they extract relations from different path strategies using multiple low-computational resources and efficient relation extraction strategies and use their proposed multi-strategy weighting calculator to weigh the relation extraction strategies to derive the final target relations. To cope with the low-resource relation extraction scenario, the relation extraction results are complemented by using prompt learning with a big model paradigm. They also consider the model to serve the commercial scenario of online education, and they propose a global rate controller to adjust and adapt the rate and throughput requirements in different scenarios, so as to achieve the best balance of system stability, computation speed, and extraction performance.