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Abstract

In order to improve the efficiency of word meaning understanding and memory in English teaching, this article studied a new vocabulary teaching method by applying Word2Vec, a neural network-based word embedding technology. Word2Vec technology can map vocabulary to high-dimensional space and represent semantic relationships between vocabulary in vector form, thereby capturing subtle semantic differences between vocabulary. By calculating the distance and direction between vectors to infer the relationship between vocabulary, this article also introduced a mobile application that integrated multiple functional modules such as vocabulary learning, memory games, learning progress tracking, and regular push notifications, providing students with a personalized learning experience. Through this application, students can learn vocabulary anytime and anywhere, and dynamically adjust their learning plans based on their learning progress and memory effects. The significance of this paper is that the accuracy and recall rates of word meaning comprehension and memory efficiency reached 96% and 98%, respectively, proving the effectiveness of this method in English teaching. This study is not only of great significance to improving the efficiency of primary school English vocabulary teaching, but also provides new ideas for the application of natural language processing and machine learning in the field of education.

Keywords

Word2Vec model English teaching understanding of word meanings memory efficiency natural language processing optimization of teaching methods

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Using Word2Vec method to improve the efficiency of word meaning understanding and memory in English teaching

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