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Transitivity Variations in Mandarin VO Compounds—A Comparable Corpus-based Approach

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

Abstract

This paper adopts a comparable corpus-based statistical approach to VO compound Variations in two varieties of Mandarin Chinese and examines the variations from a transitivity perspective. In recent years, more and more VO compounds are observed to have transitive usages. Previous studies categorize the transitivity of VO compound in a dichotomy way, while we argue that each VO actually differs in their degree of transitivity, especially when the variations between different variants of Mandarin are taken into consideration. The degree of transitivity can be measured by both transitivity frequency and its semantic/syntactic properties (follow the theory of Hopper and Thompson [1]). In our study, we compare the transitivity difference between Mainland and Taiwan Mandarin by adopting a corpus-based statistical approach. For both transitivity frequency and semantic/syntactic properties study, the results clearly show that Taiwan VO compounds have a higher degree of transitivity than the Mainland counterparts. We further argue that the higher transitivity degree in Taiwan also illustrates the conservatism of Taiwan Mandarin. This observation is consistent with the earlier study of transitivity variations of light verbs (Jiang et al. [2]) and follows the established null hypothesis in language changes that peripheral varieties tend to be more conservative.

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References

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Correspondence to Menghan Jiang .

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Jiang, M., Huang, Cr. (2018). Transitivity Variations in Mandarin VO Compounds—A Comparable Corpus-based Approach. 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_51

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  • DOI: https://doi.org/10.1007/978-3-319-73573-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73572-6

  • Online ISBN: 978-3-319-73573-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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