MusREL: A Utility-Weighted Multi-Strategy Relation Extraction Model-Based Intelligent System for Online Education

• Published in: International journal on semantic web and information systems Vol. 19; no. 1; pp. 1 - 19
• Main Authors: Zhu, Zhen, Lin, Huaiyuan, Gu, Dongmei, Wang, Liting, Wu, Hong, Fang, Yun
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2023
• Subjects: CAI; Computer assisted instruction; Education; Internet resources; Systems stability
• ISSN: 1552-6283; 1552-6291
• DOI: 10.4018/IJSWIS.329965

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.