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A Comparable Corpus-based Genre Analysis of Research Article Introductions

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Chinese Lexical Semantics (CLSW 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10709))

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Abstract

Research concerning academic genre has attracted an increasing attention from linguistics abroad and at home, ranging from a wide variety of perspectives, but there is still a shortage of research regarding the introduction part of research articles (RA). This paper makes a multi-level comparative analysis of Chinese and English RA introductions by employing the strengths of large corpora in terms of large text, multi-function and rigorous selection of texts, based on our self-compiled large-scale comparable corpus: Chinese-English Comparable Introduction Corpus (CECIC). Studies based on comparable corpora can avoid the “translationese” of parallel corpora. Findings reveal that there is still significant difference between Chinese RA introductions and the international ones and that the irregular move distribution feature of rhetorical structure in Chinese RA introductions is still prominent today, indicating that it is still necessary to strengthen Chinese RA writers’ genre awareness. That the current research chooses RA introductions, a weak point in academic studies, as the research focus is our positive effort to contribute to world genre analysis, and it will be beneficial to academic variety and theoretical innovation.

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Author information

Authors and Affiliations

  1. School of Foreign Languages, Zhengzhou University, Zhengzhou, 450001, Henan, China

    Guiling Niu

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  1. Guiling Niu
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Correspondence to Guiling Niu .

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Editors and Affiliations

  1. Peking University , Beijing, China

    Yunfang Wu

  2. National Taiwan Normal University , Taipei, Taiwan

    Jia-Fei Hong

  3. Peking University , Beijing, China

    Qi Su

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Niu, G. (2018). A Comparable Corpus-based Genre Analysis of Research Article Introductions. In: Wu, Y., Hong, JF., Su, Q. (eds) Chinese Lexical Semantics. CLSW 2017. Lecture Notes in Computer Science(), vol 10709. Springer, Cham. https://doi.org/10.1007/978-3-319-73573-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-73573-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73572-6

  • Online ISBN: 978-3-319-73573-3

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