Compressing learner language: An information-theoretic measure of complexity in SLA production data

Abstract

We present a proof-of-concept study that sketches the use of compression algorithms to assess Kolmogorov complexity, which is a text-based, quantitative, holistic, and global measure of structural surface redundancy. Kolmogorov complexity has been used to explore cross-linguistic complexity variation in linguistic typology research, but we are the first to apply it to naturalistic second language acquisition (SLA) data. We specifically investigate the relationship between the complexity of second language (L2) English essays and the amount of instruction the essay writers have received. Analysis shows that increased L2 instructional exposure predicts increased overall complexity and increased morphological complexity, but decreased syntactic complexity (defined here as less rigid word order). While the relationship between L2 instructional exposure and complexity is robust across a number of first language (L1) backgrounds, L1 background does predict overall complexity levels.

Keywords

complexity compression information theory Kolmogorov learner corpus proficiency writing

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