Abstract
This study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development among different L2 groups using relative entropy. The results show that information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level. This result is consistent with the assumption that in the course of second language acquisition, L2 learners develop towards a more complex and diverse use of language. Meanwhile, this study uses the statistics method of time series to process the data on L2 differences yielded by traditional frequency-based methods processing the same L2 corpus to compare with the results of relative entropy. However, the results from the traditional methods rarely show regularity. As compared to the algorithms in traditional approaches, relative entropy performs much better in detecting L2 proficiency development. In this sense, we have developed an effective and practical algorithm for stably detecting and predicting the developments in L2 learners’ language proficiency.
Highlights
IntroductionMeasuring learners’ second language (L2) proficiency is an important issue as regards practical language teaching, and with respect to research on L2 acquisition
Our data, which was yielded by relative entropy, reveals that the information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level
The current study used a novel ‘practical and effective’ algorithm derived from information-theoretic metrics to discern the development of L2 learners’ acquisition of language proficiency and it was based on a large-scale L2 writing corpus
Summary
Measuring learners’ second language (L2) proficiency is an important issue as regards practical language teaching, and with respect to research on L2 acquisition. To this end, in the following, both qualitative and quantitative methods have been employed, such as face-to-face interviews, standardized tests, and linguistic feature analysis and modeling. The. Different data in time series demonstrates that the data yielded by the traditional lexical and syntactic measures is one stationary type. Different data in time series demonstrates that the data yielded by the traditional lexical and syntactic measures is one stationary type In another word, these data can show few regular patterns in L2 language proficiency development. Using information-theoretical measures to detect historical changes in language is useful [26,27]
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