Abstract
A large number of online learning platforms and the explosive growth of the types and quantity of resources on the platform greatly increase the difficulty of learning content selection. Traditional language search uses resource ranking to build index, which cannot meet the needs of professional and accurate search. An intelligent search method is proposed in this paper using Bert language model for pre-training to improve the learning and reasoning ability of the machine. Based on Sentence-Bert model and the concise and effective twin network (Siamese), the sentence vector features are generated to complete the downstream search task. Finally, experiments on the online learning resources of MOOC and the State Grid were analyzed on Google Colab platform, and results show that achieve rapid keyword matching of relevant courseware names.
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He, J., Lu, Q., Tong, Y., Chen, Y. (2021). Course Classification of Online Learning Platform Based on Sentence-Bert Model. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_50
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DOI: https://doi.org/10.1007/978-981-16-3150-4_50
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