Text-mining Based English Vocabulary Learning List-a Case Study of College Entrance Examination in Taiwan

This study applied text mining techniques, machine learning approaches and statistical methods to construct a predictive model of a prioritized English vocabulary list to help nonnative English speakers prepare for college entrance English exams. Developing a method for efficiently learning English vocabulary in a limited time is an import issue. This study suggests that highly relevant and frequently repeated test items should be learned first. Although the College Entrance Examination Center (CEEC) in Taiwan has provided an approximately 7,000-word vocabulary list, the list’s suitability requires verification. Furthermore, this study constructed a vocabulary learning process model to establish a prioritized English vocabulary list for future examinees. Experimental results show that the proposed model can achieve a 78% hit ratio, which is higher than the 69% of the CEEC’s provided list.