Use of Dependency‐Annotated Learner Corpora in Measuring Syntactic Complexity for Granularity, Accuracy, Consistency, and Transparency

Implications for Research and Teaching

Bibliographic Data

ID7169348
AuthorsYujie Zhang (0000-0001-6480-4268, Faculty of Education and Social Work The University of Auckland Auckland New Zealand), Long Jiang Zhang (0000-0003-1025-1746, Faculty of Education and Social Work The University of Auckland Auckland New Zealand, corresponding author)
Year2025
Volume59
Issue2
Pages1050-1063
Publication date2025-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTESOL Quarterly (JOURNAL)
Journal identifiersISSN: 0039-8322 • E-ISSN: 1545-7249
PublisherWiley (PUBLISHER • GB)
DOI10.1002/tesq.3370
OpenAlexW4404487634
LanguageEN
Citations received2
References cited32

Syntactic complexity has long been used to gauge language learners' performance, proficiency, and development, offering language teachers valid recommendations for syllabus design and materials development. Progress in syntactic complexity research foreshadows the imperative of measuring syntactic features at a high level of granularity, which remains, however, inadequately represented in the current operationalization of syntactic complexity. Beyond this misalignment, researchers also caution against the issues of consistency and accuracy in its measurement. We thus advocate for a methodological synergy by combining automatized dependency annotation and self‐built learner corpora, arguing that this can effectively address the aforementioned concerns and promote the initiative for transparency and openness in applied linguistics. Next, we concisely showcase how to manage this method step by step, which can be done easily with tools, and some practical considerations, so researchers and teachers can compile their do‐it‐yourself corpora. We conclude by discussing the implications of adopting this method for research and teaching. How this method can figure into potential areas of inquiry is identified with examples, including task‐based language teaching, writing assessment, and additional language syntactic acquisition. Pedagogically, we illustrate how this method can help language teachers identify gaps in learners' language production, raise their language awareness, and inform learner‐centered instruction

Consistency (knowledge bases · Dependency (UML · Dependency grammar · Granularity · Linguistics · Natural language processing · Programming language · Transparency (behavior · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Psychology · Text Readability and Simplification · Topic Modeling

Tools

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