• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.433-438
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• 2021

This study investigates the relationship between lexical sophistication features and English proficiency for Korean college students. Essays from the ICNALE(International Corpus Network of Asian Learners of English) corpus were analyzed, using TAALES program. In order to examine whether or not there are statistically significant differences in lexical sophistication features across three groups, MANOVA was conducted. Results showed that the lexical sophistication features were significantly affected by English proficiency level. Essays written by Korean students with different English proficiency levels can be differentiated in terms of various lexical sophistication features including content words frequency, content words familiarity, lexical decision mean reaction time function words, hypernymy verbs, word naming response time function words, age of acquisition content words.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.691-696
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• 2022

This study explored the lexical sophistication features to distinguish the group membership of English proficiency, using the automatic analysis program of lexical sophistication. A total of 600 essays written by 300 Korean college students were extracted from the ICNALE (International Corpus Network of Asian Learners of English) corpus and a discriminant function analysis was performed using SPSS program. Results showed that the lexical features to distinguish three groups of English proficiency are SUBTLEXUS frequency content words, age of acquisition content words, lexical decision mean reaction time function words, and hypernymy verbs. High-level Korean students used frequent content words from SUBTLEXUS corpus to a lesser degree and produced more sophisticated words that can be learned at a later age and take longer reaction time in lexical decision task, and more concrete verbs.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.173-178
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• 2012

In the generated model for the recognition vocabulary, tri-phones which is not make preparations are produced. Therefore this model does not generate an initial estimate of parameter words, and the system can not configure the model appear as disadvantages. As a result, the sophistication of the Gaussian model is fall will degrade recognition. In this system, we propose the error correction system using out-of vocabulary rejection algorithm. When the systems are creating a vocabulary recognition model, recognition rates are improved to refuse the vocabulary which is not registered. In addition, this system is seized the lexical analysis and meaning using probability distributions, and this system deactivates the string before phoneme change was applied. System analysis determine the rate of error correction using phoneme similarity rate and reliability, system performance comparison as a result of error correction rate improve represent 2.8% by method using error patterns, fault patterns, meaning patterns.