A Corpus-based study of lexical bundles in the research papers of Algerian and native english speaking researchers
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Date
2022
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Université De Larbi Ben M’hidi Oum EL Bouaghi
Abstract
In the last decades, there has been a growing body of evidence to suggest that lexical bundles, as a type of formulaic sequences, contribute to the native-like fluency of the linguistic production. Lexical bundles are multi-word expressions, which are mainly defined by frequency and have been found to serve important functions in the academic discourse. However, no prior studies have been conducted to explore the use of lexical bundles in the academic discipline of Humanities and Social Sciences in Algeria before. Given this gap, the present study analyzes a corpus of 66 published research articles in the academic fields of sociolinguistics, literature, and Teaching English as a Foreign Language (TEFL). The study in hand is a comparative investigation that makes use of the corpus analysis toolkit 'AntConc' and the two analytical models of Biber et al. (1999) and Hyland (2008) in an attempt to compare the frequency, structures, and the functions of four-word lexical bundles produced by Algerian researchers on one hand, and native English-speaking researchers on the other hand. The results obtained from the corpus analysis revealed that Algerian researchers underused LBs in their research papers while deviating from the native norms as they overused VP-based bundles and underused NP+PP-based bundles, the ones that had the highest frequency in the native corpus. Nevertheless, concerning the functional distribution, the results revealed that the differently- structured LBs across the native and the Algerian sub-corpora served almost similar functions in the discourse being mainly research-oriented LBs and text-oriented LBs, with fewer bundles being participant-oriented. On account of the findings, pedagogical implications were discussed, limitations of the study as well as recommendations for future research were identified.
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Keywords
Lexical Bundles, Formulaic Sequences, Nativelike Fluency