Lexical features of engineering english vs. General english

Research output: Contribution to journalArticle

Abstract

The knowledge on the features of the English varieties is essential to understand the differences and similarities of the varieties for second language teaching and learning, either for general proficiency (EGP) or English for Specific Purposes (ESP) classes. This paper demonstrates a corpus-based comparison of the lexical features between an ESP variety (Engineering English) and a General English (GE). Two corpora are used in the study; the Engineering English Corpus (EEC) acts as the representation of the specialized language, and the British National Corpus (BNC) as the General English (GE). The analyses are conducted by employing the WordList functions of a linguistic software – Wordsmith. Discussions on the differences (or similarities) of these two corpora include general statistics, text coverage and vocabulary size. The empirical findings in this study highlight the general lexical features of both corpora. The analyses verify that the Engineering English has less varied vocabulary, but higher text coverage than the GE; in other words, most of the words are used repeatedly throughout the EEC. Thus, this study further emphasizes the importance of corpus-based lexical investigations in providing empirical evidences for language description.

Original languageEnglish
Pages (from-to)106-119
Number of pages14
JournalGEMA Online Journal of Language Studies
Volume17
Issue number3
DOIs
Publication statusPublished - 2017

Fingerprint

engineering
vocabulary
language
coverage
statistics
linguistics
Teaching
learning
evidence
English for Specific Purposes
Corpus-based
software

Keywords

  • Corpus
  • ESP
  • Language description
  • Lexical features
  • Specialized corpus

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Literature and Literary Theory

Cite this

Lexical features of engineering english vs. General english. / Khamis, Noorli; Abdullah @ Ho Yee Beng, Imran Ho.

In: GEMA Online Journal of Language Studies, Vol. 17, No. 3, 2017, p. 106-119.

Research output: Contribution to journalArticle

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