Research and Application of Key Vocabulary Extraction Algorithm in English Vocabulary Learning

Abstract

An effective key vocabulary extraction is significant for English Language Learning, assisting the learners to achieve proficiency in English. However, the process of vocabulary extraction is often challenging and multifaceted, leading to inaccurate vocabulary acquisition, which reduces the learner's proficiency. To address these issues, we presented a novel key vocabulary extraction using the combination of Radial Basis Function Neural Network (RBFNN) and Emperor Penguin Optimizer (EPO). The primary objective of this research is to identify the most relevant vocabulary from the huge English language corpus to assist learners in improving their proficiency levels and learning process. The proposed work commences with the collection of English corpus and the collected database undergoes pre-processing steps like tokenization, stop word removal, and stemming to improve the efficiency of subsequent analysis. Then, the developed EPO-RBFNN was designed to extract the most informative and relevant features from the pre-processed database. The RBFNN module was trained using the pre-processed database to learn and capture the semantic patterns and interconnections within the corpus, while the EPO was employed for selecting the vocabulary sequence considering their relevance and importance in English learning. The proposed framework was implemented in the Python tool, and the results are evaluated in terms of accuracy, precision, recall, and f-measure. Furthermore, a comparative assessment was made with existing vocabulary extraction methodologies to validate the outcomes. The comparative analysis showcases that the proposed strategy outperformed the conventional models, making it a suitable solution for key vocabulary extraction.