English Quasi-Numeral Classifiers

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As a corpus-based typological cognitive study of English binominal quantitative expressions based on English-Chinese comparison, the book discovers functional equivalents of Chinese numeral classifiers in English, i.e. 'Quasi-Numeral Classifiers' (QNCs) and unveils the categorisation process reflected by five cases of Dimensionality-based QNCs.


This book is an interdisciplinary study of English binominal quantitative constructions based on English-Chinese comparison. Taking three perspectives, i.e. a functional-typological perspective, a cognitive approach, and a corpus-based method, it aims to unveil the hidden categorisation process behind the usage of English binominal quantitative constructions and to reveal the language universal in cognising the concepts of 'Quantity' and 'Quality'. It argues against treating Chinese and English as members of two opposing typological camps concerning quantification modes ('classifier languages' versus 'non-classifier languages') and advocates to view the two languages as lying within a more extended and inclusive system, viz. a system of quantification and categorisation modes, or a Quantity-Quality continuum.


Autorentext

Xu Zhang is currently a lecturer in the School of English Studies, Beijing Language and Culture University, China. She obtained her PhD degree in Linguistics from Lancaster University, UK. in 2009.


Inhalt

Introduction Classification of Classifiers and English QNCs: A Typological Perspective Categorisation by NCs and QNCs: A Cognitive Perspective A Corpus-Based Methodology 1-D QNC Categories: Case Studies of Thread, Strip, and Column 2-D QNC Categories: Case Studies of Sheet and Slice Conclusion

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Weitere Informationen

  • Allgemeine Informationen
    • Sprache Englisch
    • Herausgeber Peter Lang
    • Gewicht 681g
    • Untertitel A Corpus-Based Cognitive-Typological Study
    • Autor Xu Zhang
    • Titel English Quasi-Numeral Classifiers
    • Veröffentlichung 25.08.2017
    • ISBN 3034328184
    • Format Fester Einband
    • EAN 9783034328180
    • Jahr 2017
    • Größe H231mm x B155mm x T25mm
    • Anzahl Seiten 380
    • Lesemotiv Verstehen
    • Auflage 1. Auflage
    • GTIN 09783034328180

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