2022
DOI: 10.2478/amns.2022.1.00027
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Design of language assisted learning model and online learning system under the background of artificial intelligence

Abstract: This paper aiming at the problems of scattered operation types, poor extraction effect of learning resources and incomplete assignment of learning tasks in the online language learning platform designs a language assisted learning model under the background of artificial intelligence, proposes an English learning resource extraction algorithm based on the integration of LSA and N-gram models and then proposes a weighted multi-task learning model and it is optimized to improve the sparsity of learning variables… Show more

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Cited by 2 publications
(2 citation statements)
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“…Specifically, the Weighted Naive Bayes algorithm dynamically adjusts weights during the model training process, driven by data, to optimize model parameters. This enables it to better capture and utilize information in the training data [4] . This approach not only improves the model's ability to recognize specific user learning behaviors but also enhances the adaptability of the English mobile learning system to different learning environments.…”
Section: Naive Bayes Algorithm and Weighting Techniquementioning
confidence: 99%
“…Specifically, the Weighted Naive Bayes algorithm dynamically adjusts weights during the model training process, driven by data, to optimize model parameters. This enables it to better capture and utilize information in the training data [4] . This approach not only improves the model's ability to recognize specific user learning behaviors but also enhances the adaptability of the English mobile learning system to different learning environments.…”
Section: Naive Bayes Algorithm and Weighting Techniquementioning
confidence: 99%
“…Machine learning model analysis has increasingly become an important reference for people's judgment and decision making, and is increasingly known and used. Various analytical tools and analytical methods based on machine learning models have emerged [1][2][3][4]. The idea of big data, which itself has undergone the process from experiment to practice and from niche to mass, is also increasingly known [5][6][7][8].…”
Section: Introductionmentioning
confidence: 99%