Algorithm Design and Implementation of Automatic Assessment and Expansion of English Vocabulary

Automatic assessment and expansion of English vocabulary play a crucial role in language learning and proficiency evaluation. In this paper, we propose the use of an Optimized Bi-directional Long Short-Term Memory Deep Learning (Obi-LSTM-DL) model for these purposes. We conduct experiments using a c...

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Veröffentlicht in:Journal of Electrical Systems Jg. 20; H. 6s; S. 2211 - 2220
1. Verfasser: Niu, Xiaoqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Paris Engineering and Scientific Research Groups 29.04.2024
Schlagworte:
Accuracy
Algorithms
Comparative analysis
Deep learning
Effectiveness
Optimization
Performance evaluation
ISSN:1112-5209
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Zusammenfassung:Automatic assessment and expansion of English vocabulary play a crucial role in language learning and proficiency evaluation. In this paper, we propose the use of an Optimized Bi-directional Long Short-Term Memory Deep Learning (Obi-LSTM-DL) model for these purposes. We conduct experiments using a comprehensive vocabulary dataset and evaluate the model's performance across various scenarios. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance, with an average increase in accuracy of 1.5% for every 1,000 words added to the vocabulary. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model, with an average increase in accuracy of 3.2% over traditional models.
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ISSN:1112-5209
DOI:10.52783/jes.3135