What more can empirical contextual data tell about the real usage of words and collocations : A case study of the qià () cluster with Chinese Gigaword data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Journal / PublicationChinese Language and Discourse
Online published27 Jun 2022
Publication statusOnline published - 27 Jun 2022


This article presents a corpus-based distributional analysis of the usage patterns of a cluster of words and compounds containing the morpheme qià (恰) ‘just, exactly’, by the aid of an extended concordancer to retrieve representative collocations from their adjacent contexts in Chinese Gigaword. Upon a survey of the historical evolution of the qià cluster with exemplar data and an overview of existing proposals to account for their usages in terms of expectational match, our distributional analysis is conducted to identify the salient collocational or contextual features that lead to a number of interesting findings. Substantial evidences are provided for clarifying the non-word status of qià rú (恰如) and qià sì (恰似) and their similarities, the exchangeability of qiàhǎo (恰好) and qiàqiǎo (恰巧), distinct collocational preferences of the adverbs qià (恰), qiàqià (恰恰) and the others with different subsets of verbs, the prosodic requirement of an even number of syllables for a qià-adverb and its main verb, and the contrastive popularity of qiàqià (恰恰) vs qiàdàng (恰当) to reveal different usage tendencies between speakers in Taiwan and the Mainland. All these novel findings and insights about the subtle (dis)similarities in the usage and meanings of the qià (恰) cluster suggest that distributional analysis of contextual collocations using large-scale language data remains a powerful tool that can complement other analytical approaches for the advancement of lexical semantic research.

Research Area(s)

  • corpus-based distributional analysis, qià (恰) cluster, usage pattern, collocational preference, Chinese Gigaword