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Intelligent Natural Language Processing: Trends and Applications pp 677–710Cite as

A Cross-Cultural Corpus Study of the Use of Hedging Markers and Dogmatism in Postgraduate Writing of Native and Non-native Speakers of English

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

This study investigates the frequency of hedged propositions in academic writing, which are produced by both native (NSs) and non-native speakers (NNSs). To this end, two corpora, which represent native and non-native writings respectively, are compiled and investigated using contrastive interlanguage analysis (CIA). This computer-aided investigation, which involves comparing quantitative and qualitative data, is adopted to identify what the most frequent hedging markers, used by native and non-native writers, are, and whether there is any significant difference between the frequencies of these markers in both writings. This research is an attempt to fill a gap in literature, as there is a paucity of studies written on corpus analysis in the Middle East. The findings suggest that non-native speakers underuse hedges and the quality of these hedges is usually not so high as those of the native speakers.

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Author information

Authors and Affiliations

  1. Faculty of Education, The British University in Dubai, Dubai, UAE

    Rawy A. Thabet

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  1. Rawy A. Thabet
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Correspondence to Rawy A. Thabet .

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Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Thabet, R.A. (2018). A Cross-Cultural Corpus Study of the Use of Hedging Markers and Dogmatism in Postgraduate Writing of Native and Non-native Speakers of English. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_32

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  • DOI: https://doi.org/10.1007/978-3-319-67056-0_32

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  • Online ISBN: 978-3-319-67056-0

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