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

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.

Acknowledgments

We thank the anonymous reviewers of JQL for their detailed and insightful comments on the earlier versions of this manuscript. Also, our sincere thanks go to Prof. Haitao Liu, Prof. Jingyang Jiang, Dr. Wenping Li and Dr. Heng Chen for their helpful suggestions and invaluable support.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Social Science Foundation of China [Grant Number: 21BYY113].