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In:Mathematical Modelling in Linguistics and Text Analysis: Theory and applications
Edited by Adam Pawłowski, Sheila Embleton, Jan Mačutek and Aris Xanthos
[Current Issues in Linguistic Theory 370] 2025
► pp. 118–127

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Development of mean dependency distance in Czech L2 texts

 | University of Ostrava
 | University of Ostrava
 | University of Ostrava
Published online: 13 October 2025
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https://doi.org/10.1075/cilt.370.10han
Abstract
Syntactic analysis is a crucial part of second language acquisition studies. Over the past 30 years, researchers have developed numerous measures to evaluate the syntactic development of second language writers, concentrating on clause, t-unit, or sentence length. Recent attention has shifted towards measures that reflect sentence dependency structures, as demonstrated by mean dependency distance (MDD). Employing MDD, we actively analyse the syntactic development of Czech L2 texts using 5,721 samples that cover A1–C1 proficiency levels. We compare results with a reference corpus of texts authored by Czech native speakers (SKRIPT2012; Šebesta et al. 2013). We explore cross-linguistic influence by comparing Slavic language speakers and other language speakers at the same proficiency levels. Findings indicate a positive correlation between language proficiency and MDD, with significant differences between adjacent levels. Slavic L1 groups consistently demonstrate higher MDD than their other language speaker counterparts.
Article outline
  • 1.Introduction
  • 2.Language material and methodology
    • 2.1Language material
    • 2.2MDD computation
    • 2.3Data processing
  • 3.Results
    • 3.1Development of MDD across the language proficiency levels
    • 3.2Differences of MDD in Slavic and other language groups
  • 4.Conclusion
  • References
References (23)
References
Al-Shehri, Saleh & Christina Gitsaki. 2010. Online reading: A preliminary study of the impact of integrated and split-attention formats on L2 students’ cognitive load. ReCALL 22(3). 356–375. Google Scholar logo with link to Google Scholar
Chen, Xinying & Miroslav Kubát. 2024. Quantifying syntactic complexity in Czech texts: An analysis of mean dependency distance and average sentence length across genres. Journal of Quantitative Linguistics 31(3). 260–273. Google Scholar logo with link to Google Scholar
Fedorenko, Evelina, Rebecca Woodbury & Edward Gibson. 2013. Direct evidence of memory retrieval as a source of difficulty in non-local dependencies in language. Cognitive Science 37(2). 378–394. Google Scholar logo with link to Google Scholar
Gao, Nan & Qingshun He. 2023. A corpus-based study of the dependency distance differences in English academic writing. SAGE Open 13(3). 1–12. Google Scholar logo with link to Google Scholar
Gerdes, Kim, Bruno Guillaume, Sylvain Kahane & Guy Perrier. 2018. SUD or Surface-Syntactic Universal Dependencies: An annotation scheme near-isomorphic to UD. In Marie-Catherine de Marneffe, Teresa Lynn & Sebastian Schuster (eds.), Proceedings of the second workshop on universal dependencies (UDW 2018), 66–74. Brussels: ACL. Google Scholar logo with link to Google Scholar
Guillaume, Bruno. 2021. Graph Matching and Graph Rewriting: GREW tools for corpus exploration, maintenance and conversion. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 168–175, Online. Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Jiang, Jingyang & Jinghui Ouyang. 2017. Dependency distance: A new perspective on the syntactic development in second language acquisition: Comment on “Dependency distance: A new perspective on syntactic patterns in natural language” by Haitao Liu et al. Physics of Life Reviews 21. 209–210. Google Scholar logo with link to Google Scholar
Kisselev, Olesya, Aleksandr Klimov & Mikhail Kopotev. 2022. Syntactic complexity measures as linguistic correlates of proficiency level in learner Russian. In Agnieszka Leńko-Szymańska & Sandra Götz (eds.), Complexity, accuracy and fluency in learner corpus research, 51–80. Amsterdam: Benjamins. Google Scholar logo with link to Google Scholar
Kuiken, Folkert, Ineke Vedder, Alex Housen & Bastien De Clercq. 2019. Variation in syntactic complexity: Introduction. International Journal of Applied Linguistics 29(2). 161–170. Google Scholar logo with link to Google Scholar
Li, Wenping & Jianwei Yan. 2021. Probability distribution of dependency distance based on a treebank of Japanese EFL learners’ interlanguage. Journal of Quantitative Linguistics 28(2). 172–186. Google Scholar logo with link to Google Scholar
Liu, Haitao. 2008. Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science 9(2). 159–191. Google Scholar logo with link to Google Scholar
Liu, Haitao, Chunshan Xu & Junying Liang. 2017. Dependency distance: A new perspective on syntactic patterns in natural languages. Physics of Life Reviews 21. 171–193. Google Scholar logo with link to Google Scholar
Mann, Henry. B. & Ransom D. Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18(1). 50–60. Google Scholar logo with link to Google Scholar
Meng, Leran. 2023. English language learning students’ second language acquisition: Cognitive factors and supporting strategies. In Proceedings of the 3rd International Conference on Educational Innovation and Philosophical Inquiries, Vol. 3, 817–827. Oxford: EWA Publishing. Google Scholar logo with link to Google Scholar
Nogolová, Michaela, Radek Čech, Michaela Hanušková & Miroslav Kubát. 2023. The development of sentence and clause lengths in Czech L2 texts. Korpus — gramatika — axiologie 28. 22–37. Google Scholar logo with link to Google Scholar
Ouyang, Jinghui, Jingyang Jiang & Haitao Liu. 2022. Dependency distance measures in assessing L2 writing proficiency. Assessing Writing 51. 100603. Google Scholar logo with link to Google Scholar
Šebesta, Karel et al. 2013. SKRIPT2012: Akviziční korpus psané češtiny — přepisy písemných prací žáků základních a středních škol v ČR. Prague: Charles University.Google Scholar logo with link to Google Scholar
2014. CzeSL-SGT — a corpus of non-native speakers’ Czech with automatic annotation, version 2 from 28 Sep 2014. Prague: Charles University.Google Scholar logo with link to Google Scholar
Straka, Milan. 2018. UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task. In Proceedings of CoNLL 2018 shared task: Multilingual parsing from raw text to Universal Dependencies, 197–207. Brussels: CL.Google Scholar logo with link to Google Scholar
Welch, Bernard. L. 1947. The generalization of ‘student’s’ problem when several different population variances are involved. Biometrika 34(1/2). 28–35. Google Scholar logo with link to Google Scholar
Yan, Hengbin & Yinghui Li. 2019. Beyond length: Investigating dependency distance across L2 modalities and proficiency levels. Open Linguistics 5(1). 601–614. Google Scholar logo with link to Google Scholar
Zeman, Daniel et al. 2022. Universal Dependencies 2.10. LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL). Prague: Charles University. [URL]
Zipf, George K. 1949. Human behavior and the principle of least effort: An introduction to human ecology. New York: Hafner.Google Scholar logo with link to Google Scholar
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