A Corpus-Based Study of the Dependency Distance Differences in English Academic Writing

Nan Gao and Qingshun He

SAGE Open, 2023, vol. 13, issue 3, 21582440231198408

Abstract: Dependency distance has increasingly become a key measure of interest in cross-linguistic corpus studies from multiple perspectives. Based on a syntactically annotated corpus of 400 PhD dissertation abstracts written by native English (L1) and English as a foreign language (L2) academic writers, the current study investigated the mean dependency distance (MDD) variation across language backgrounds and disciplines, which is followed by a grammatical description based on fine-grained indices related to particular syntactic structures. The findings include: (1) L2 academic writers produce an averagely longer MDD than L1 academic writers because of their heavy use of prepositional phrases; (2) The MDD of the linguistics abstracts is significantly longer than that of the physics & chemistry abstracts because of the relatively higher syntactic complexity of the language of linguistics. The findings suggest that MDD can effectively differentiate academic texts with different language backgrounds and disciplines, that both L1 and L2 academic writers write under the constraint of dependency distance minimization, and that L2 PhD dissertation writers have achieved native-like writing proficiency in extending nominal structures.

Keywords: academic writing; corpus-based; dependency distance; syntactic complexity (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231198408

DOI: 10.1177/21582440231198408

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