Authors: Xu Hongzhi, Jiang Menghan, Lin Jingxia and Chu-Ren Huang

Title: Light verb variations and varieties of Mandarin Chinese: Comparable corpus driven approaches to grammatical variations

Abstract: This article presents a classification and clustering based study to account for the differences among five Chinese light verbs (congshi, gao, jiayi, jinxing, and zuo) as well as their variations in Mainland China Mandarin (ML) and Taiwan Mandarin (TW). 

Light verb variations and varieties of Mandarin Chinese: Comparable corpus driven approaches to grammatical variations

Xu Hongzhi, Jiang Menghan, Lin Jingxia and Chu-Ren Huang

(Institute of Corpus Studies and Applications, Shanghai International Studies University, Shanghai, China; Department of Chinese and Bilingual Studies, Hong Kong Polytechnic University, Kowloon, Hong Kong; Department of Chinese Language and Literature, Peking University, Beijing, China; School of Humanities, Nanyang Technological University, Singapore 639818; Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong )

Abstract: This article presents a classification and clustering based study to account for the differences among five Chinese light verbs (congshi, gao, jiayi, jinxing, and zuo) as well as their variations in Mainland China Mandarin (ML) and Taiwan Mandarin (TW). Based on 13 linguistic features, both competition and co-development of these light verbs are studied in terms of their distinct and shared collocates. The proposed method discovers significant new grammatical differences in addition to confirming previously reported ones. Most significant discoveries include selectional restrictions differentiating deverbal nominals and event nouns, and degrees of transitivity of VO compounds. We also find that most variations between Mainland China Mandarin and Taiwan Mandarin are in fact differences in tendencies or preferences in contexts of usage of shared grammatical rules.

Keywords: Chinese light verbs; language variations; clustering; classification; corpus approach

Link:

https://www.degruyter.com/document/doi/10.1515/cllt-2019-0049/html