A Corpus-Based Study of Lexical Collocations of Keywords Found in Online News Articles
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Abstract
This corpus-based study investigates lexical collocations of keywords found in online news articles, aiming to equip high school students in Thailand with the linguistic tools necessary for English admission exams. The primary objectives of this investigation are twofold: 1) to compile a list of 100 essential exam preparation keywords from the self-constructed corpus; and 2) to identify the lexical collocations involving the first ten keywords. The study categorizes these keywords and collocations by using 350 online news articles from CNN. The corpus was developed by aggregating online news articles and analyzing them using AntConc software. The findings reveal a keyword distribution where nouns are predominant (57%), alongside verbs, adjectives, and adverbs, reflecting the dynamic nature of news language. The lexical collocations were identified and categorized from the top one hundred keywords into combination types, such as noun + noun, noun + verb, verb + noun, and adjective + noun. These findings not only enrich vocabulary learning for students, but they also guide educators in designing more targeted teaching materials. This study concludes with pedagogical implications and suggestions for further research in the field of English language education, emphasizing the critical role of authentic textual analysis in preparing students for successful academic and professional futures.
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