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Syntactic Word Order and Typological Parameters in Indonesian and Uzbek: a Computational Corpus-based Comparison of News Discourse

Year: 2026
Type: article
Abstract: This study investigates syntactic word order and selected typological parameters in Indonesian and Uzbek through a comparative analysis of news discourse. The study examines naturally occurring sentences collected from Indonesian and Uzbek online news sources, represented by Kompas.com and Khalq So‘zi, respectively. Indonesian is commonly characterized as a subject–verb–object (SVO) language, whereas Uzbek is generally described as a subject–object–verb (SOV) language. However, actual news discourse may exhibit alternative constituent orders influenced by information structure, topicalization, emphasis, voice, argument structure, and other syntactic and discourse-related factors. The study therefore does not assume that either language is restricted to a single canonical word order. Instead, it systematically identifies and compares the distribution of major constituent-order patterns in the two languages. Using Python-based computational methods and a graphical user interface developed for corpus processing, the study collects, cleans, annotates, classifies, and statistically analyzes sentence-level data. The analysis focuses on subject, object, and verb positions as well as selected syntactic parameters, including basic constituent order, object–verb ordering, subject position, topicalization, voice, and constituent-order variation. Descriptive statistics are used to establish the frequency and distribution of syntactic patterns, while comparative analyses are employed to identify similarities and differences between Indonesian and Uzbek. The study contributes a computationally assisted empirical account of Indonesian–Uzbek syntactic variation and demonstrates how corpus-based programming can be used to investigate cross-linguistic word-order patterns in authentic news discourse.
Source: HSR (London)
Language: English
Open Access status: diamond
OpenAlex ID: W7225845085
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