Can the Probability Distribution of Dependency Distance Measure Language Proficiency of Second Language Learners?

Abstract

It has been found that the length distribution of many linguistic units fits well the same model, the Zipf-Alekseev function. In this article, we aimed to find out whether this holds for English learners’ interlanguage and whether the parameters in probability distribution of dependency distance can measure the language proficiency of second language learners. We selected 367 participants of English learners of nine consecutive grades and fitted different probability distribution models to dependency distances of their writings in English and of self-built contrastive dependency treebanks based on Wall Street Journal Corpus. It was found that: (1) the Zipf-Alekseev distribution well captures the probability distribution of dependency distance of each grade and native speakers; (2) the probability distribution of dependency distance well measures second language learners’ language proficiency at different learning stages; (3) high-level learners don’t present exactly the same parameters in the probability distribution of dependency distance as those of native speakers, which means learners’ language proficiency is not as high as that of English native speakers and second language learners’ syntactic acquisition process is always constrained by the tendency of dependency distance minimization. This study corroborates that quantitative linguistic methods can be well utilized in second language acquisition researches.