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      Establishing a COVID-19 lemmatized word list for journalists and ESP learners

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      Indonesian Journal of Applied Linguistics
      Universitas Pendidikan Indonesia (UPI)

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          Abstract

          The aim of this research is two-fold; first, to explore the most frequent COVID-19 inspired words in medical news reporting contexts, and second, to classify them into different categories. This paper adopts a corpus-based approach to build a lemmatized academic word list (AWL) inspired by the COVID-19 pandemic. Factiva was used to retrieve the pandemic-related articles published in News Rx from January 1 - October 31, 2020. A total number of 18,249,093-word corpus was compiled. The corpus linguistic software program Wordsmith (WS-6) (Scott, 2012) was used to generate a word list based on the complied corpus. Subsequent to compiling, lemmatizing, and analyzing the AWL, six categories were identified, namely, acronyms and abbreviation, diseases, COVID-19, biology, medicine, and scientific disciplines, all of which are of essential use for media workers, ESP learners of journalism, medicine, nursing, pharmacy, and allied health sciences. Building such a discipline-specific glossary will be of special pedagogical value for health journalists, textbook writers and curriculum designers, instructors, and ESP learners in the health sciences field. One of the major contributions of this research is establishing lemmas of a large set of AWL. This set can be utilized by news media workers, health communication specialists, and ESP learners. Lemmatization will ensure rapid dissemination of the word list and its integration in the linguistic system through derivation and other word-formation processes.

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          Journal
          Indonesian Journal of Applied Linguistics
          Indonesian J. Appl. Linguist.
          Universitas Pendidikan Indonesia (UPI)
          2502-6747
          2301-9468
          January 31 2022
          January 31 2022
          : 11
          : 3
          : 577-588
          Article
          10.17509/ijal.v11i3.37103
          c97587ac-7f1f-410c-87d0-e3a662877a56
          © 2022

          http://creativecommons.org/licenses/by-sa/4.0

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