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Profiling a microeconomics noun collocation list

A corpus-based approach

Bibliographic Data

ID21757313
AuthorsMengyu He (0000-0001-9229-3223, Hubei University of Economics), Leng Hong Ang (0000-0001-5688-7753, Universiti Sains Malaysia, corresponding author)
Year2023
Volume41
Issue2
Pages191-209
Publication date2023-04-03
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueSouthern African Linguistics and Applied Language Studies (JOURNAL)
Journal identifiersISSN: 1607-3614 • E-ISSN: 1727-9461
PublisherNational Inquiry Services Center (NISC) (PUBLISHER)
DOI10.2989/16073614.2022.2117708
OpenAlexW4323262656
LanguageEN
Citations received1
References cited62

Collocations are essential for language learning because using collocations naturally and accurately constitutes a key indicator of academic writing success. Past studies have examined collocations in academic genres and created lists of collocations for pedagogical use. However, studies that compile collocation lists often use a wide-angle approach to identify general collocations. There have been concerns about using general academic multiword constructions (MWCs) in English for specific purposes (ESP) courses as there are formalities and conventions in academic texts which are characterised by discipline-specific MWCs. This corpus-based study proposes a common and pedagogically useful microeconomics noun collocation list (MNCL). The MNCL is based on a corpus of microeconomics research articles comprising 1.6 million words. The MNCL is the result of an innovative procedure that involves a four-step selection method. This study produces: (1) a list of the 1 125 most common noun collocations (noun + noun, noun + verb, verb + noun, adjective + noun) in microeconomics research articles identified and classified using the most commonly used nouns, and (2) new findings regarding the types of noun collocations. The findings of this study have pedagogical implications on how collocations should be perceived and approached in ESP classrooms

Adjective · Grammar · Linguistics · Natural language processing · Noun · Verb · Computer Science · Discourse Analysis in Language Studies · Natural Language Processing Techniques · Philosophy · Second Language Acquisition and Learning · Artificial Intelligence

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Unique citing works1
Citations per year0,5
Citation span2024 - 2024 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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