A diachronic corpus-based study of noun phrase complexity in Pakistani medical research articles (1988–2024)

Phrasal complexity plays a crucial role in L2 writing and research, with noun phrase modifiers, in particular, contributing to more compact and dense expressions. This study adopts a mixed-methods approach to examine diverse noun phrase modifiers based on Biber’s noun phrase complexity features. The analysis is conducted on a specialized corpus comprising 1,008 research articles (3,001,321 tokens) published between 1988 and 2024 across ten Higher Education Commission Journal Recognition System (HJRS)-indexed Pakistani medical journals. Data were tagged and analyzed using the Multidimensional Analysis Tagger (MAT), while LexiConc and Python were employed to calculate frequency and diversity. Findings, presented as average normed rates per 100 tokens, reveal that seven of the nine identified modifiers are widely used and exhibit a consistent increase over time in the noun phrases of the sampled articles. Furthermore, the MAT statistical outcomes, replicating Biber’s Multi-Dimensional Analysis (MDA), highlight the co-occurrence of linguistic features across the corpus. Specifically, low scores on dimension 1, high scores on dimension 3, and low scores on dimension 6 reflect the frequent use of nominalizations and other noun phrase modifiers, contributing to the compressed style of medical writing. These statistically significant diachronic trends in noun phrase modifiers hold important implications for researchers, journal editors, and writers in medical linguistics.