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(Non)metonymic Expressions for government in Chinese: A Mixed-Effects Logistic Regression Analysis

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Abstract

This paper focuses on the alternative choice between literal and metonymic expressions for the concept government from an onomasiological point of view. With the help of mixed-effects logistic regression analyses, this study models the binary designations for government with the data from a self-built corpus of texts from newspapers and online forums in Mainland Chinese and Taiwan Chinese. Mixed-effects models also provide a way of accommodating the random-effect factors such as the verbs in the data. The statistical results unveil that the choice of literal vs. metonymic designations is a result of the complex interplay of a number of conceptual, grammatical/discursive and lectal factors and no single decisive factor would determine people’s onomasiological choice.

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Notes

  1. 1.

    A lectal variety refers to all types of language varieties or lects, such as regional dialects, sociolects, basilects, acrolects, idiolects, registers and styles [5, 6].

  2. 2.

    Texts from the four resources were captured with the help of several Python scripts. The People’s Daily and the Tianya Club can be accessed at http://paper.people.com.cn/rmrb and www.tianya.cn/bbs/. The United Daily News and the PTT can be accessed at http://udn.com/NEWS/mainpage.shtml and http://www.ptt.cc/index.bbs.html. We thank Tom Ruette for his help on the Python script for downloading texts from the Tianya Club.

  3. 3.

    For the Tianya Club, the first post of most thematic discussions is a copied news article which is then followed by original posts, so we excluded all first posts from the Tianya Club to build the online forum dataset for Mainland Chinese.

  4. 4.

    The country name list was extracted from http://zh.wikipedia.org/wiki/国家列表. The capital name list was extracted from http://zh.wikipedia.org/wiki/各国首都列表. The official residence names are cases like 中南海 Zhongnanhai “the official residence of Chinese government leaders”, 白宫 bai-gong “White House”, 唐宁街 tang-ning-jie “Downing Street”. Note that some countries or capitals have different linguistic expressions in the two language varieties, for instance, Washington has the Chinese equivalents 华盛顿 hua sheng dun in MC and 华府 hua fu in TC, and New Zealand has the Chinese equivalents 新西兰 xin-xi-lan in MC and 纽西兰 niu-xi-lan in TC. All possible linguistic variants are included in the list of place names for this study.

  5. 5.

    When discussing affairs between Mainland China and Taiwan, the expression cross-strait is often used as a general term in reference to the Taiwan Strait.

  6. 6.

    We have segmented the titles of all texts based on the Chinese Lexical Analysis System from the Institute of Computing Technology (ICTCLAS, http://ictclas.org/index.html). The topic classification was based on the title of each text. Instance Based Learning classifies unseen texts into the category of its most similar text in a manually annotated corpus. Similarity between two texts is measured by representing each text as a vector in a Euclidian space and taking the cosine of the angle between the two vectors. For the current task, a 3-Nearest Neighbor approach was used. A formal introduction to Instance Based Learning can be found in Chapter 8 of Mitchell [31]. We thank Tom Ruette for his help on the topic-identification programming script.

  7. 7.

    The C index (or concordance index), ranging from 0.5 to 1, is used to measure the predictability of the logistic regression model. It is the “area under the ROC curve” to quantify the power of the model’s predicted values to discriminate between positive and negative cases. A C index of 1 indicates perfect prediction; a C index of 0.5 indicates random prediction [38]. The Somer’s Dxy provides a rank correlation between the predicted probability and the observed responses ranging from 0 to 1.

  8. 8.

    The 3D-graph visualization of the interaction was implemented in R [40]. Three more remarks need to be made about the z axis: “First, the plots are artificial in the sense that our predictors can assume only two possible values and that the only situations that can actually occur are represented by the four corners of the surfaces in the plot. Second, although in the plots the z axis is represented on a logit scale, we will describe the effects in terms of increased or decreased predicted probability of [Meto=yes]. Third, four small dots in the corners of each plot indicate the zero position on the z axis. This helps us to see whether joint effects are positive or negative” [29].

  9. 9.

    The interpretation of the relation between place name metonymy and emotional involvement towards specific governments is, of course, tentative. A careful and refined measurement of people’s emotional attitudes is a must for a better appreciation of such relation. Apparently, the emotional involvement has both positive and negative sides. One may suspect that the two sides of emotional involvement could have quite different impacts on the choice of literal vs. metonymic expressions for government. In the present study, we have not distinguished the specific effects of different kinds of emotional involvement, as it is very difficult to measure people’s emotional attitudes toward the concept government with the limited contexts. At the same time, individual journalists and online forum users may not have homogeneous types or degrees of emotional involvement towards governments. In addition, as Milić and Vidaković have proved, several reporter-related factors can influence the usage of capital for government, for example, the reporter’s whereabouts (abroad or home) and standpoint on the issue being reported [17]. One possible direction for further study on this issue would be a sentiment analysis of each text from which an observation is retrieved, which we would measure the positive, negative or neutral emotional attitudes of the journalist or online forum user toward the government in question, i.e. he/she is supporting or criticizing the government or stating a government-related affair in a neutral way.

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Author information

Authors and Affiliations

  1. Institute of Linguistics, Shanghai International Studies University, Shanghai, China

    Weiwei Zhang

  2. Research Unit of Quantitative Lexicology and Variational Linguistics, University of Leuven, Leuven, Belgium

    Weiwei Zhang

  3. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Dirk Geeraerts & Dirk Speelman

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  1. Weiwei Zhang
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  2. Dirk Geeraerts
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  3. Dirk Speelman
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Correspondence to Weiwei Zhang .

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Editors and Affiliations

  1. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Dirk Speelman

  2. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Kris Heylen

  3. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Dirk Geeraerts

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Zhang, W., Geeraerts, D., Speelman, D. (2018). (Non)metonymic Expressions for government in Chinese: A Mixed-Effects Logistic Regression Analysis. In: Speelman, D., Heylen, K., Geeraerts, D. (eds) Mixed-Effects Regression Models in Linguistics. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-69830-4_7

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