FREQUENCY PROFILE OF CAUSATIVE CONSTRUCTIONS IN CONTEMPORARY ENGLISH AND UKRAINIAN MEDIA DISCOURSE: LINGUOSTATISTICS AND AUTOMATED SELECTION ALGORITHM
DOI:
https://doi.org/10.32782/folium/2026.9.30Keywords:
causative constructions, complementtaking predicate, usage-based approach, media discourse, linguostatistics, natural language processing, syntactic compressionAbstract
The article deals with a functional and linguostatistical analysis of causative constructions in contemporary English and Ukrainian media discourse. The relevance of the study lies in the need for an objective investigation of syntactic structures across macro-textual arrays using natural language processing (NLP) tools. The methodological framework integrates the usage-based approach. Drawing on this, we interpret usage frequency as a key determinant in the formation of grammatical patterns. The purpose of the study is to identify the frequency profile and pragmatics of target predicate verbs based on a 1,000-sentence sample. To aggregate and process a macro-textual array (over 43,000 web publications), we developed and applied an innovative Python-based algorithm (utilizing the spaCy library and DependencyMatcher), enabling precise automated syntactic parsing followed by expert validation. The analysis reveals a distinct cross-linguistic asymmetry: the relative frequency of predicate verbs expressing persuasion/ requests and coercion/commands in English media discourse is three to four times higher than that of their Ukrainian counterparts. The research establishes that non-finite complementation serves as a highly productive mechanism of syntactic compression in English. Conversely, in Ukrainian, the infinitive complex functions as a marker of institutional directiveness in the formal news register. Furthermore, an indicative trend toward employing constructions with inanimate and abstract causers is identified in both languages. The conclusions indicate that the choice of causative constructions serves as an effective tool for pragmatic positioning, while the developed algorithm lays the groundwork for automated analysis of discourse patterns in large textual arrays.
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