Abstract
The goal of this corpus-based study was to look at the word lists, collocations, and lexical bundles of logistics research articles (LRAs) in order to get a better understanding of the real English language used in the logistics area for both academic and specific purposes. The stated concerns were then investigated using four corpus tools: AntConc, Sketch Engine, ConcGram 1.0, and RANGE. A logistics journal was used to compile 21 LRAs at random. The findings revealed that the top list of words found in corpus tools differed. After that, each tool displayed a comparable list of the top 10 nouns. And the top list of lexical bundles was generated differently by each tool.
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