Dependency Distance and Its Probability Distribution: Are They the Universals for Measuring Second Language Learners’ Language Proficiency?

Abstract

ABSTRACT Previous studies have shown that dependency distance and its probability distribution can be applied as syntactic indicators of English as interlanguage. However, the universal application of these indicators has not been verified from the perspective of language typology. The issues are addressed in the present study based on a treebank of Chinese interlanguage of English and Japanese native speakers. The findings are as follows: (1) with the improvement of L2 proficiency, the MDDs of learners with different native language backgrounds gradually approach that of the target language in different patterns, and dependency distance is of universal significance as a metric to measure the development of interlanguage’s syntactic complexity; (2) Chinese interlanguage also follows the principle of least effort, and its probability distribution of dependency distance, like those of natural languages, presents a power–law distribution, which can successfully fit the Zipf-Alekseev distribution; (3) the right truncated modified Zipf-Alekseev distribution can be used to measure Chinese interlanguage proficiency, and the fitting parameters of the probability distribution of dependency distance as a metric of interlanguage proficiency are also of universal value.