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CORPUS-BASED STUDY ON THE LEXICAL BINDLES IN WRITTEN ENGLISH OF CHINESE ADVANCED ENGLISH LEARNERS

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Authors: FENG SHI, TIRZAH ZUBEIDAH ZACHARIAH, NORAZEAN SHAARI

Year

2024

Paper ID

4958

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

200

Citations

0

Abstract

Lexical bundles, as recurrent fixed expressions, play a vital role in achieving fluency and naturalness in language output. Corpus linguistics provides a robust framework for analyzing authentic language use and identifying patterns in large corpus. This paper employs the corpus research approach, combined with theories of second language acquisition, to explore the overall usage patterns of lexical bundles in the writing of Chinese advanced English learners. AntConc software and self-compiled FoxPro programs are utilized to extract word frequency data and perform concordance line searches based on two sampled corpora: the British National Corpus and the Chinese Learner English Corpus. Comparative analysis reveals that although there is no significant difference between Chinese advanced learners of English and native speakers in terms of the overall token frequency of high-frequency lexical bundles, considerable differences in type frequency and distribution patterns are significant. Factors such as first language transfer and avoidance strategies significantly influence the usage patterns of lexical bundles among Chinese students. Therefore, the findings of this paper help provide insights for designing academic English courses for new graduate students, and in the design process of all relevant courses and teaching practices, instructors should take the impact of these distinctive patterns into considerations.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • Lexical bundles, as recurrent fixed expressions, play a vital role in achieving fluency and naturalness in language output.

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