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Compressing learner language

An information-theoretic measure of complexity in SLA production data

Bibliographic Data

ID11655774
AuthorsKatharina Ehret (0000-0003-1117-3495, University of Freiburg, corresponding author), Benedikt Szmrecsanyi (0000-0001-8844-6602, KU Leuven)
Year2016
Volume35
Issue1
Pages23-45
Publication date2016-09-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSecond language Research (JOURNAL)
Journal identifiersISSN: 0267-6583 • E-ISSN: 1477-0326
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0267658316669559
OpenAlexW2526517594
LanguageEN
Citations received22
References cited28

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

Algorithm · Computational linguistics · Kolmogorov complexity · Linguistic sequence complexity · Linguistics · Measure (data warehouse · Natural language processing · Second-language acquisition · Structural complexity · Computer Science · Language and cultural evolution · Natural Language Processing Techniques · Psychology · Text Readability and Simplification · Artificial Intelligence

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Unique citing works22
Citations per year2,44
Citation span2017 - 2026 (10)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 22

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