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Metaphor and the Social World 1:1 (2011), 90–112

Variation in the (non)metonymic capital names in Mainland Chinese and Taiwan Chinese

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Weiwei Zhang, Dirk Speelman, Dirk Geeraerts

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Nov. 2010

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manuscript submitted-please do not quote

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Acknowledgement: The authors thank Tom Ruette for the programming script of topic identification and Jack Grieve for the proofreading. The authors also thank members of the QLVL research unit at the University of Leuven for valuable discussions of this study. And finally, we would like to thank the two anonymous reviewers for constructive and detailed comments on an earlier version of the article. All remaining

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errors are the authors'.

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ABSTRACT This paper examines the (non)metonymic usage of capital names in news articles

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from Mainland Chinese and Taiwan Chinese and shows that this phenomenon is actually more complex than might have been expected. We annotated capital names extracted from a self-built news corpus with insights from previous studies on place name metonymies in Cognitive Linguistics and identified factors that would influence their (non)metonymic usage. To quantitatively explore the data, logistic regression analysis was employed. The statistical results reveal that the variation in the (non)metonymic capital names is a result of an intricate interplay of a number of conceptual, lectal and discursive factors: (1) more metonymic capital names are found

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in subject than non-subject position and in political than non-political news topic; types of capitals may influence their metonymic usage; (2) differences between Mainland Chinese and Taiwan Chinese cannot be ignored, especially for the interpretation of a specific metonymy, i.e. CAPITAL FOR GOVERNMENT; (3) the (non)metonymic usage of a capital name is also determined by its sequencing and location in discourse. We hope this study may shed some light on the usage-based trend of current Cognitive Linguistics, i.e. investigating metonymy in authentic linguistic data by a range of empirical methodologies.


Variation in the (non)metonymic capital names in Mainland

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Chinese and Taiwan Chinese

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1 Introduction In Cognitive Linguistics, metonymy has garnered much interest in recent years. The primary interest of metonymy research focuses on the definition/classification of metonymy (cf. Radden & Kövecses, 1999; Barcelona, 2003; Peirsman & Geeraerts, 2006; Panther & Thornburg, 2007) or its interaction/differentiation with metaphor (cf.

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Goossens, 1990; Barcelona, 2000, 2002; Geeraerts, 2002; Barnden, 2010). From a methodological perspective, previous studies on metonymy are mainly based on linguistic intuition or dictionaries (e.g. Barcelona, 2003, 2004; Jing-Schmidt, 2008), which makes it hard to depict an accurate picture of the actual distribution of phenomena (Markert & Nissim, 2003, p.176). In current usage-based Cognitive

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Linguistics, scholars tend to analyze metonymy in real language use, e.g. adopting a corpus-based approach (e.g. Deignan, 2005; Stefanowitsch & Gries, 2006; Allan, 2009). Among them, Markert and Nissim’s (2003, 2006) research on the metonymic usage of location names and organization names is intriguing. In these studies, Markert and

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Nissim present an annotation scheme for metonymic location/organization names. At the same time, Markert and Nissim (2003) unveil some factors influencing the distribution of metonymies, i.e. the particular name, its associations, and the text type. Their research contributes greatly to the (automatic) recognition of metonymy in realworld texts (cf. Markert & Nissim, 2009; Nissim & Markert, 2003), but still leaves open the question of what other factors may influence the distribution of metonymies. Also in line with the usage-based trend of Cognitive Linguistics, Brdar-Szabó& Brdar (2003), Brdar (2006), and Brdar & Brdar-Szabó (2009) explore the (non)metonymic use of place names from a cross-linguistic perspective. They analyze a specific referential metonymy, i.e. CAPITAL FOR GOVERNMENT in news articles from

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English, German, Croatian, and Hungarian daily newspapers and find that the crosslinguistic availability of this metonymy "seem[s] to be the result of an intricate interplay of conceptual, grammatical, and discourse-pragmatic factors" (Brdar & Brdar-Szabó, 2009, p. 249). Their research enlightens us to a comparative study among different lectal verities1 in order to determine the "cause" behind variations in metonymic usage in different lectal varieties. Given the usage-based nature of Cognitive Linguistics, Geeraerts (2005) argues that Cognitive Linguistics necessarily has to incorporate social variation, e.g. lectal variation,


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as a crucial aspect of linguistic structure. Many Cognitive Sociolinguists (cf. Kristiansen & Dirven, 2008; Geeraerts, Kristiansen & Peirsman, 2010) claim that it is necessary to take lectal variation into consideration when we deal with certain linguistic/cognitive phenomena. However, there has been relatively little analysis of a cognitive phenomenon like metonymy conducted with an eye to social variation. Inspired by the research of Brdar & Brdar-Szabó(2009) and Markert & Nissim (2003) and the appeal of Cognitive Sociolinguistics, the present study aims to explore variation in (non)metonymic capital names in two of lectal varieties of Chinese, i.e. Mainland Chinese (MC) and Taiwan Chinese (TC), and to identify the factors

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influencing the variation. Metonymic usage of capital names, or more generally place names, is ubiquitous in many languages (cf. English in Barcelona (2004); English in Markert & Nissim (2003); English, German, Croatian, and Hungarian in Brdar & Brdar-Szabó(2009); Norwegian in Halverson & Engene (2010)). This cognitive process can also be found in Chinese, such as the following:

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(1) a.台北本周宣布启动社子岛开发计划。 Taipei announced the start-up plans for developing Shezi Dao this week.

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b. 2004 年，北京对全市城市雕塑普查。 Beijing made a general investigation to its urban sculpture in 2004. In example (1a), Taipei actually denotes the municipal government of Taipei; in example (1b), Beijing refers to the municipal government of Beijing. Both capital names have metonymic readings under a general metonymic pattern, i.e. CAPITAL FOR GOVERNMENT. Obviously, a capital name literally indicates a locative sense with a geographical feature, as in example (2):

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(2) a. 一名许姓乘客从台北赶来，他说自己是铁路迷。 A passenger named Xu came from Taipei, and he said he was a railfan.

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b. 12 月 29 日，北京某商场，前来选购虎玩具的顾客络绎不绝。 On December 29th, in a supermarket of Beijing, an endless stream of people went to buy toy tigers. The concern of the present study is the metonymic usage (e.g. example (1)) and nonmetonymic usage (e.g. example (2)) of capital names in real-world language. In other words, this study adopts a semasiological approach, answering the questions: Given a lexical item, i.e. a capital name, what meanings does it express? Does it convey a metonymic meaning or not? (Geeraerts, 1997, p. 17). This study also addresses the issue:


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What factors may influence the distribution of (non)metonymic meanings for capital names? In Section 2, we will introduce how we designed the study, illustrating the data resources, and the variables coded in the dataset. Then, in Section 3, the statistical results will be discussed and interpreted. Finally, we will draw conclusions based on the statistical analysis and give some directions for further study in Section 4.

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2 Design of the study 2.1 The data The data for our study were taken from news articles written in two of lectal varieties

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of Chinese. For MC, we chose People's Daily3 in the time period between November 1, 2009 and January 16, 2010. For TC, we used four newspapers from the United Daily News group4, i.e. United Daily News, United Evening News, Economy Daily News, and Upaper in the time period between December 11, 2009 and January 16, 2010.

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We extracted a capital name list with 209 capitals from the internet5 and searched for all the capital names on the list in the self-built news corpus6. We obtained 16658 valid instances with capital names in total. All instances were extracted with one sentence of context. For those ambiguous cases, we may always trace back to the original news articles for further context.

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Then, we conducted several filtering steps. First, we removed the instances with capital names which always denote a locative sense in news leads. For these capital names, there is no variation of their meaning, like in example (3).

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(3) a. （新华社贝尔格莱德1月10日电） (Xinhua News Agency, Belgrade, January 10)

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b. 北京：中国总理温家宝周一说... BEIJING: Chinese Premier Wen Jiabao said on Monday… Second, if a capital name denotes an address or is a constituent of a proper noun, it was removed from the dataset also due to the impossibility of variation. There is no alternative meaning except the locative meaning for these cases. Example (4a)

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illustrates an address case and (4b) is an example of a capital name as a constituent of a proper name. In contrast to (4b), Beijing in (4c) is just a modifier of the proper name Tsinghua University, therefore (4c) is kept in our dataset for further exploration. The differentiation between a constituent of a proper name (like (4b)) and a modifier of a proper name (like (4c)) is sometimes subtle in practice. We consulted the homepage of the proper name or Wikipedia to determine whether the capital name we encountered constituted a case of (4b) or (4c). (4) a. 北京宣武区35号


No. 35, Xuan Wu District, Beijing. b. 北京语言文化大学 Beijing Language and Culture University

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c. 北京清华大学 Beijing Tsinghua University The overall information in our database before and after manual filtering is given in Table 1 and Figure 1. Only the part with the label "free" in Figure 1, in the sense that it can be freely used with a metonymic meaning or locative meaning, survived after the manual filtering.

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TABLE 1 ABOUT HERE

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FIGURE 1 ABOUT HERE

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2.2 The variables As presented in Table 1, in total 7857 instances were kept in our dataset after the

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manual filtering. We annotated them for the following variables. 2.2.1 The response variable Meto

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In the analysis of the data, we statistically modeled the (non)metonymic use of capital names. What concerns us here is the binary usage of a capital name, i.e. metonymic use or non-metonymic use. The binary usage of a capital name is called the response variable in our statistical model and given the label Meto. This response variable has two possible values, yes or no (metonymic use or non-metonymic use).

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This variable was encoded manually by the authors according to the meaning of a capital name in the context. In our dataset of 7857 instances we have 769 cases of Meto=yes and 7088 cases of Meto=no. Obviously this is a heavily biased distribution with a proportion of 0.0979 (cases of Meto=yes) versus a proportion of 0.9021 (cases of Meto=no).

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For the metonymy identification procedure, we used an adaptation of MIP (Pragglejaz Group 2007, Steen et al. 2010) and the annotation schema of Markert and Nissim (2003). The main annotator was the first author of the paper, and for some less straightforward cases, agreement was reached intersubjectively by the discussion among the three authors. The procedure for metonymy identification employed in this study is as follows: 1. Read the entire sentence to establish a general understanding of the meaning.


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2. (a) For the capital name in the text, establish its meaning in context, that is, how it applies to an entity, relation, or attribute in the situation evoked by the text. (b) For each capital name, determine its basic contemporary meaning, i.e. a locative meaning for all cases. (c) Decide whether the capital name's contextual meaning contrasts with the basic meaning but has a contiguous relationship with it. Consult previous research on place names (e.g. Markert & Nissim, 2003, 2009; Brdar & BrdarSzabó, 2009; Halverson & Engene, 2010) for potential contiguous relationship

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candidates, such as a contiguous relationship between PLACE and GOVERNMENT or PEOPLE or EVENT etc. 3. If yes, mark the capital name as metonymical (Meto=yes). Then note

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its metonymic targets, like GOVERNMENT (national government, municipal government), EVENT (summit, sports activity, exhibition...), PEOPLE (officials,

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citizens...) etc7.

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2.2.2 The predictors We included the predictors that represent conceptual, lectal and discursive factors in

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the statistical model.

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Conceptual factors (1) The predictor Anim The variable Anim stands for "the syntactic position and the animacy of the subject

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required by the predicate in a sentence or subject-predicate phrase". It has three possible values: when a capital name is in a non-subject position, the value of Anim is assigned irrelevant; when a capital name is in the subject position, the value is either yes or no, which stands for the predicate requiring an animate or not-necessarily animate

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subject respectively. This variable is encoded manually. Normally, the actions of humans, animals as well as human collectives (e.g. citizens, teams) require animate

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subjects. We also consulted the Great Modern Chinese Dictionary (2006) for identifying the basic sense of the predicate. In our dataset, we have 6929 cases of Anim=irrelevant, 400 cases of Anim=no and 528 cases of Anim=yes. In Example (5a), Beijing is the subject of the sentence. The predicate claim denotes an action of humans and normally requires an animate subject. Thus, the value of Anim in this instance is yes. (5b) and (5c) are the cases of Anim=no (i.e. subject position, notnecessarily animate subject required by the predicate) and Anim=irrelevant (i.e. non-subject position) respectively.


(5) a. 北京已表明准备允许外资企业在中国挂牌的态度。 Beijing has claimed that it plans to allow overseas-funded enterprises to start their business in China.

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b. 华府将是主要的障碍。 Washington will be the major obstacle. c. 欧盟领袖上周末在布鲁塞尔召开紧急会议。 European Union leaders held emergency sessions in Brussels last weekend. The purpose of this variable is twofold. Firstly, it provides the syntactic function of capital names. It makes it possible to notice the variation of (non)metonymically used capital names in a subject (Anim=no or yes) versus non-subject (Anim=irrelevant) position. Secondly, it introduces a conceptual/grammatical

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influence on the metonymic usage, that is, the animacy of the subject required by the predicate may have some correlation with the metonymic usage of capital name (i.e. Anim=yes vs. Anim=no). (2) The predictor Topic The variable Topic stands for topics of news articles. We assigned eight possible values for Topic: social issues (1999 cases), business and economics (1954 cases), culture and living (1622 cases), politics (1406 cases),

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military issues (106 cases), , sports (342 cases), science and education (287 cases), other (141 cases). The topic identification of news articles was done

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semi-automatically8. The purpose of this variable is to test whether the news topic may influence the (non)metonymic use of a capital name. We expect that political news may have higher frequency of metonymically used capital names than others, because CAPITAL FOR GOVERNMENT is a very conventional metonymy recognized by many previous researchers (e.g. Brdar & Brdar-Szabó, 2009; Milić & Vidaković 2007). (3) The predictor Cap_group The variable Cap_group stands for "capital groups". We classified the 119 capitals

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in our dataset into six groups: self (3992 cases), Asia (1201 cases), indif (1050 cases), counterpart (769 cases), gpb (697 cases), and warzone (148 cases). It is encoded manually. Beijing was assigned the value of self for instances from the Mainland newspaper and the value of counterpart for instances from the Taiwan newspapers. While, Taipei was assigned the value of self for instances from the Taiwan newspapers and the value of counterpart for instances from the Mainland newspaper. The capital group with the value of gpb refers to the capitals of those "global power broker" countries, like permanent members of UN Security Council9.


Cap_group=Asia includes the capitals of those countries which are geographically

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neighbors of China, like Ulan Bator, Tokyo, New Delhi and other countries in Asia, like countries from the ASEAN. Capitals of the countries which Chinese people may have indifferent emotional attitude towards were assigned the value indif, like Canberra, Amman, Buenos Aires, Dakar etc. Finally, Cap_group=warzone was assigned to

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the capitals of those countries which were still in chaos caused by war, like Jerusalem, Islamabad, Kabul etc. Value assignment for this variable involves some complications. For example, some capitals may have multiple values, e.g. Beijing belongs to self, counterpart and

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gpb groups. To make it simple, we excluded Beijing from gpb group. Also, we

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presumed that people from Mainland China and Taiwan might have similar emotional attitudes towards some countries (e.g. Cap_group=indif), which inevitably biased

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our data. In fact the emotional attitudes towards certain countries may diverge to some extent between people from the two lectal varieties. In brief, the coding of this variable is a just simplification for the purpose of testing Brdar & Brdar-Szabó's claim (2009) of

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"a relatively greater density of metonymic use occurs with highly emotionally charged capital names than that of emotionally neutral capital names" (p. 235). Based on this claim, we expect capitals of "global power broker" countries, to which people may have

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least closeness with themselves, would have the highest frequency of metonymic usage, while one's own capital, which is closest to the ego, should be underused as the metonymic source (cf. Brdar, 2006).

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Lectal factors (4) The predictor LangVar LangVar deals with the lectal effect on a cognitive phenomenon, i.e. "variation of

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(non)metonymic usage in two lectal varieties ". It was encoded automatically and has two possible values: LangVar=MC (3346 cases) and LangVar=TC (4511 cases).

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The purpose of this variable is to test whether MC and TC have different distributions of (non)metonymic use of capital names; or in other words, test whether

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the lectal factor plays a role in people's metonymic mechanism or not.

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Discursive factors The main purpose of discursive variables is to explore the persistency (cf. Szmrecsanyi, 2006) of metonymy in discourse. According to Brdar-Szabó& Brdar (2003), "one of the most important discourse-pragmatic functions of metonymy is to enhance cohesion and coherence of the utterance" (p. 96). Further, Brdar (2007) and Brdar-Szabó& Brdar (2003) pointed out that one of several strategies to solve the problem of the preservation of the topic in the following discourse is to produce a whole


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string of repeatedly used metonymies. We designed a number of variables to detect the discursive function of metonymy, allowing different spans for the string of repeated used metonymies, like a sentence or a discourse. (5) The predictor Juxta_meto The variable Juxta_meto refers to "the (non)metonymic use of a juxtaposed place

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name (all place names including but not limited to capital names) of the capital name in the sentence". When the capital name has no juxtaposed place name, the value was assigned Juxta_meto=irrelevant. When there are one or more juxtaposed place names, the predictor has two possible values, yes (with one or more metonymic

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juxtaposed place names) or no (without a metonymic juxtaposed place name). This

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variable was encoded manually following the metonymy identification procedure in Section 2.2.1. In our dataset of 7857 instances, we have 6746 cases of Juxta_meto=irrelevant, 952 cases of Juxta_meto=no and 159 cases of Juxta_meto=yes. For example, Beijing is the capital in question in example (6). The

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juxtaposed place names are Taipei and Shanghai severally in (6a) and (6b). Taipei has a metonymic meaning of “the government of Taiwan”, thus (6a) was encoded with Juxta_meto=yes; however, with a locative sense of Shanghai, (6b) was assigned

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Juxta_meto=no.

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(6) a. 北京得看懂，台北也得看懂，否则容易擦枪走火。 Beijing has to understand; Taipei has to understand as well. Otherwise, there will be an accidental conflict.

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b. 目前富邦产险在大陆北京、上海二地设有代表处。 So far, Fubon Financial has set up representative offices in Beijing and

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Shanghai of Mainland. This variable allows us to ascertain the persistency of metonymy within a span of one sentence. We expect that within one sentence, if its juxtaposed place name is used metonymically, the capital name would be used metonymically as well to maintain the

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topic-continuity of the discourse. (6) The predictor Country This variable encodes if the capital name is used with or without its own country name as a pre-modifier. It was encoded manually and has three possible values. If the capital name is pre-modified by its country name, the instance was assigned the value of Country=yes (419 cases, see example (7), the capital name Kabul is pre-modified by its country name Afghanistan in Chinese); if not, it was assigned the value of


Country=no (6718 cases); if the capital name and its country name are identical, we encoded it as Country=dualrole (720cases), like Singapore, Luxembourg, etc.

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(7)11 月 9 日，阿富汗喀布尔，建筑工人在建造防爆墙。 On November 9th, the builders were constructing the explosion-proof wall in

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Kabul, Afghanistan. The hypothesis here is that if the capital name is pre-modified by a "bigger" place name, like a country name, it tends to denote its literal meaning, because a "bigger" place name preceding a "smaller" place name is the habitual form in Chinese to express locative meaning. (7) The predictor PreMetoNo_cat

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To detect the persistency of metonymy in discourse, we further extended the span of potential string of repeatedly used metonymies to the whole news article and introduced a variable PreMetoNo_cat, which indicates whether the capital name at hand has

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already been used metonymically in the preceding stretch of text (including the news title). There are four possible values for this variable. PreMetoNo_cat=A (7097

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cases) indicates that there is no identical capital name used metonymically preceding this instance. PreMetoNo_cat=B (555 cases) indicates that the frequency of

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identical capital name used metonymically preceding this instance is one or two. PreMetoNo_cat=C (123 cases) was assigned to the instance which had three, four or

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five identical capital names used metonymically preceding it. Lastly, if there are more than five identical capital names used metonymically preceding this instance, it was assigned PreMetoNo_cat=D (82 cases).

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Actually in the statistical model, for this variable, we used the Helmert coding, which compares each subsequent level to the mean of the previous levels. For example, the third level (PreMetoNo_cat=C) will be compared with the mean of the first two levels, and the fourth level (PreMetoNo_cat=D) will be compared to the mean of the

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first three levels. If the capital name in question has been precedingly used metonymically, we may

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expect that it has more possibility to be used metonymically, because the journalist intends to maintain the cohesion and coherence of the utterance. (8) The predictor Title_meto_exact This variable indicates whether the identical capital name in the title of the article from which the instance was extracted is metonymically used or not. When there is no identical capital name in the title or the instance itself is the title, the value was assigned Title_meto_exact=irrelevant (6811 cases). We have 284 cases of


Title_meto_exact=yes, and 762 cases of Title_meto_exact=no. The

used metonymically in the main body. (9) The predictor Locus

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While the previous four predictors of discursive factors largely deal with the sequencing of metonymy in discourse, the predictor Locus tackles the location of potential metonymy in discourse. With possible values Mainbody (7523 cases) and

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assignment of values for this variable was done automatically. We expect that if the capital name has already been used metonymically in the title of the article (i.e. Title_meto_exact=yes), it may have more possibility to be

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Title (334 cases), Locus simply encodes whether an instance is drawn from the

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main body or the title of the news article. It was encoded automatically. According to Papafragou (1996), one communicative reason for using metonymies lies in that the processing effort may be smaller than that for a literal expression of the metonymic sense. In other words, metonymy can be regarded as an economical means

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to express information during communication. Thus, with words limits, the title is expected to have a higher density of the metonymy than the main body.

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2.2.3 Summary of the variables So far, we have introduced all variables retained in the statistical model. Table 2

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TABLE 2 ABOUT HERE

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provides an overview of predictor conditions and predicted effects of all variables.

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3 The results of the statistical analysis For this study, we applied logistic regression analysis, which is a statistical modeling with a binary response variable (such as in this case study Meto=yes or Meto=no)

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and with multiple explanatory variables. In this section, we will interpret the output of the logistic regression analysis for our dataset.

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3.1 The logistic model First of all, we simply explain some important aspects of the logistic regression model (cf. Speelman & Geeraerts, 2009). From a technical perspective, the logistic model actually does not predict a probability p of having e.g. Meto=yes but rather a derived value, called logit for Meto=yes, which is the estimates of the logistic regression. The p can be calculated by the formula p=exp (logit)/(1+exp(logit)). Thus, when we interpret the logistic regression, we may translate the effects on logit into the effects on probabilities by this formula. In our statistical model, the higher logit the model predicts, the higher probabilities it


predicts for Meto=yes; the lower logit the model predicts, the lower probabilities it predicts for Meto=yes.

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Table 3 shows the results from the logistic regression analysis. The left column indicates the predictor values of variables selected in the final model; those values not listed are the baseline values of these predictors. The order of predictors listed in this table reflects the order in which the stepwise regression procedure selected the variables for inclusion in the model. The different estimates in the second column express a difference in predicted logit when the variable at hand has the value listed in the left

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column, as opposed to when it has the baseline value, and while controlling for all other predictors. The estimate of (intercept) (i.e. -6.92) is the logit of a model with all predictors assigned the baseline values. We may simply add to this (intercept)

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estimate the estimates for all predictors which do not have a baseline value in this category of instances in order to calculate the predicted logit for other categories of

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instances. The p-values in brackets tell us how certain we are about the actual existence

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of an effect of that predictor on the response variable. We choose p=0.05 as the significance level in this paper. We obtained the model by running a forward stepwise regression technique in order to automatically select the relevant variables (as in Table 2) from all the variables we encoded and allowing two-way interactions. The model predict the logit for Meto=yes. By a model simplification procedure, we removed those non-significant

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interactions. Only one interaction seemed to be justified, i.e. the interaction between Anim and Country.

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TABLE 3 ABOUT HERE

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We may interpret the main effects in the model firstly. For example, all other things being equal (i.e. when controlling for other predictor variables), the model predicts the logit to be 0.98 higher for Locus=Title than the baseline case of

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Locus=Mainbody, and the logit to be 2.42 lower for Topic=culture and living than the baseline case of Topic=politics. For the two interaction terms, their joint effect should be expressed by the interplay of the three estimates (Anim, Country and Anim:Country) of the model. Still, we would like to comment on the general information of our statistical model. We list the summary of the logistic regression analysis in Table 4. The generalized R squared describes the proportion of the variation in our response variable that is explained by the predictors. The C value is also a measure of predictive powder, where


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values close to 1 indicate that the model has good predictive ability. We concede that validating the model by the step-down algorithm across 200 bootstrap runs indicates we have been slightly overfitting the model to the dataset. Thus, for interpreting the statistic results of some factors in the model, we do not insist on absolute certainty. We will explain this aspect when we interpret the factors in Section 3.2.

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TABLE 4 ABOUT HERE

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3.2 Interpretation of the results We interpret the statistic results following the order of these predictors introduced in Section 2.2.2 (cf. Table 2).

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Conceptual factors ID 1. The predictor Anim. From Table 3, we may see an interaction between Anim and Country. However, the validation of the model by bootstrapping suggests that we

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run the risk of slightly overfitting the model if we including the interaction Anim:Country in the model. Thus, we will not elucidate the interaction here for saving space. A modest claim may be made for Anim alone: we encounter more

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metonymic usage of capital names when they are in the subject position than non-

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subject position. At the same time, the metonymic usage of capital names has positive correlation with those predicates which require animate subjects (Anim=yes). This

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claim recalls the statement of Brdar & Brdar-Szabó(2009, p. 238) that "metonymically used names of capitals seem more natural as subject" as well as the finding of Markert & Nission (2009) that metonymies occur more often in subject position. ID 2. The predictor Topic. Our results confirm the hypothesis that political news

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has a higher frequency of metonymically used capital names than other topics except sports news. According to the positive estimate (0.03), sports news seems to have a slightly higher possibility of metonymic capital names than political news, however the difference is not significant (p=0.912) at all. In general, the expectation that political

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news favors metonymically used capital names is confirmed by the statistic results. ID 3. The predictor Cap_group. We find that capital names from the self group disfavor metonymic usage more than the capital names from gpb group, counterpart group, and countries which people may have neutral attitude (indif group). At the same time, there is no significant difference between the metonymic usage of capital names of the self group and capitals from the Asia group and the warzone group. As we have illustrated above, the conclusion here may be just a


speculation on this issue, because it is not easy work to estimate people's emotional attitude towards different countries.

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Lectal factors ID 4. The predictor LangVar. If we ran a penalized model (penalty=0.1) to compensate for the risk of overfitting the data, the predictor LangVar turned out to be

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insignificant in the penalized model. Thus, we do not make any decisive claim for this predictor based on the statistic results of the logistic regression model, e.g. TC favors metonymic usage of capital name generally. However, to detect the lectal variation between MC and TC, we may zoom in on a specific subset of the whole dataset, i.e.

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instances concerning a general metonymy, i.e. CAPITAL FOR GOVERNMENT, which consists of two sub-patterns, CAPITAL FOR NATIONAL GOVERNMENT (325 cases) and CAPITAL FOR MUNICIPAL GOVERNMENT (280 cases). In this specific subset, we find variation between these two lectal varieties.

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Firstly, is there any difference between MC and TC concerning the aforementioned two sub-patterns? We made a mosaic plot looking at the relationship between LangVar and the two sub-patterns (see Figure 2). From the plot, we see that capital

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names in TC favors expressing NATIONAL GOVERNMENT other than MUNICIPAL 10 GOVERNMENT, which opposes to MC . The Chi-square test shows that the variation

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FIGURE 2 ABOUT HERE

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found here is with statistical significance.

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Then, a closer scrutiny will also reveal considerable lectal variation in the distribution of the two sub-patterns for those cases involving Beijing and Taipei (see Figure 3). In MC, Beijing has 158 instances with the metonymic target of MUNICIPAL GOVERNMENT OF BEIJING, while only 4 instances with the target of NATIONAL GOVERNMENT OF CHINA; in TC, 5 instances use Beijing metonymically for MUNICIPAL GOVERNMENT OF BEIJING, while 130 instances for NATIONAL GOVERNMENT OF CHINA.

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The metonymic usage of Taipei in these two lectal varieties is also intriguing. None of the instances with Taipei is used metonymically in MC. However, 64 cases of metonymic Taipei are found with the target of MUNICIPAL GOVERNMENT OF TAIPEI and 27 found with the target of GOVERNMENT OF TAIWAN in TC. Fisher's exact tests indicate that the variations are statistically significant. FIGURE 3 ABOUT HERE


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We speculate that the lectal differences found here might not be arbitrary, but rather are ideologically motivated. Considerable research has explored the relationship between language and ideology in the press, including Fowler (1991), Van Dijk (1998), Kuo & Nakamura (2005). The consensus is that there is a close relationship between language and ideology in media discourse (Kuo & Nakamura 2005, p. 395). Precisely at this point, the linguistic choices made in news articles may carry ideological meaning. In this study, the different political stances between the newspapers originating from the two lectal varieties might trigger different distributions of (non)metonymic usage of capital names, to the least extent, for some specific metonymic targets. As mentioned

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above, the variation is extremely obvious for Beijing and Taipei. The different political stances between the Mainland newspaper and Taiwan newspapers might play a crucial role here. People's Daily advocates "One-China policy" and insists that Taiwan is a part of China without a doubt, thus it is not surprising that there is no metonymic usage of Taipei for GOVERNMENT OF TAIWAN, or even MUNICIPAL GOVERNMENT OF TAIPEI in

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instances from MC.

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Discursive factors ID 5, 6, 7,8. The predictors Juxta_meto, Country, PreMetoNo_cat,

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Title_meto_exact. The estimate of Juxta_meto=yes is 10.97 in the model,

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which shows that capital names with metonymic juxtaposed place names favor metonymic usage to a large extent. This finding squares with Milić and Vidaković (2007, p.265)'s claim that "a greater readiness of Croatian journalists to use the metonym [CAPITAL FOR GOVERNMENT] if it is coordinated with another one of this type used for a foreign government". For PreMetoNo_cat, we find that the more

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metonymic usage of a capital name in the previous part of the text, it has more tendency to be metonymically used in the latter part. In addition, according to the estimate of Title_meto_exact, we may conclude that if there is a metonymically used capital name in the title of the news, the identical capital name favors metonymic usage in the

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main body. According to our statistical results, we ascertain the persistency of metonymy in discourse, which supports the discussion on the relationship between metonymy and topic-continuity in Brdar (2007) and Brdar & Brdar-Szabó(2009). Regrettably, the interaction Anim:Country as well as the main effect Country turned out to be insignificant if we apply a penalty to the model for compensating the overfitting. Thus, we may not make any conclusion for this predictor.


ID 9. The predictor Locus. It is confirmed that capital names favor metonymic

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usage when they are in the title other than in the main body of news articles. This finding also upholds the effective and economical communicative function of metonymy.

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4. Conclusions and further perspective In Section 3, we have discussed the results of the statistical analysis. A number of conclusions and implications can be drawn from the foregoing discussion. Firstly, in general, the use of metonymy is much more complex than might have been expected. The marked differences observed in the statistic results show that the

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(non)metonymic usage of capital names in news is not an isolated linguistic or cognitive phenomenon but is a result of an intricate interplay of several factors, such as conceptual factors and discursive factors. We also tentatively suggest that when we deal with some specific metonymies, e.g. CAPITAL FOR GOVERNMENT, we should take lectal factors into account.

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Secondly, the important lectal variation found in the interpretation of certain specific metonymies, i.e. CAPITAL FOR GOVERNMENT, suggests a rapprochement between Cognitive Linguistics and the tradition of sociovariationist research of language usage. Both the predominantly semantic perspective and the usage-based nature of Cognitive

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Linguistics foster its embrace of a social conception of language (cf. Geeraerts, 2005, Geeraerts, Kristiansen & Peirsman, 2010). Thus, when we deal with a cognitive phenomenon, like metonymy, we cannot ignore its social environment. Thirdly, those factors which are confirmed to play a role in the (non)metonymic distribution of capital names in our analysis might shed light on the automatic identification of metonymies in real-world texts. For example, besides text type and word-specific behavior as proposed in Markert & Nissim (2003), the discursive factors identified in this study might also serve as influential factors when we try to develop an (automatic) metonymy recognition model. Finally, from a methodological perspective, this study responds to the call of

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Cognitive Linguistics to adopt an empirical methodology. For the data collection, we used non-elicited language data from a self-built news corpus; for the data analysis, we employed quantitative methods to test hypotheses. The methodological implication here further echoes the second implication above, in the sense that "an empirical, usagebased approach in Cognitive Linguistics cannot evade the study of language variation" (Geeraerts, 2005, p. 166).


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Far from being a complete exploration of the relevant phenomena, this study is a first step towards a multifactor analysis of (non)metonymic usage of capital names. More work still needs to be done in future. First of all, for the ideological motivation of lectal variation we discussed in Section 3.2, we may further carry out a comparative study between instances from two ideologically opposed Taiwan newspapers, i.e. the pro-reunification United Daily News and the pro-independence Liberty Times. If ideology indeed plays a role in distribution of (non)metonymic capital names, we expect a significant difference between the metonymic and nonmetonymic usage of some capital names, like Beijing, in these two

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newspapers. Secondly, in this paper all the work we have done is from a semasiological perspective. We would like to design an onomasiological study looking at the choice of expressing one concept by a literal phrase or a metonymic capital name. For example, the concept CHINESE GOVERNMENT can be expressed by the phrase the Chinese

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government literally or by the metonymic capital name, Beijing. What factors may influence our choice of the two remains to be explored.

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NOTES

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1 A lectal variety refers to “any given speech style, including those categories traditionally labelled as standard varieties, regional dialects, sociolects, basilects, acrolects, registers and styles” (Kristiansen, 2008, p.47). 2 This and all following examples in this paper are from the self-built newspaper corpus. 3 People’s Daily is a daily newspaper published worldwide. For this study, we chose the Chinese-language edition. As the Communist Party of China's mouthpiece, it generally provides direct information on the policies and viewpoints of the Party. 4 The four newspapers chosen from the United Daily News group are daily newspapers published in Taiwan. In terms of political alignment, newspapers of UDN group are strongly Pan-Blue, i.e. pro-reunification, and conservative.

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5 The capital list was extracted from the website as following: http://en.wikipedia.org/wiki/List_of_national_capitals. Some countries have more than one capitals, like Bolivia (Sucre as the constitutional capital and La Paz as the administrative capital). Note that some capitals have different linguistic expressions in the two language varieties, for instance, 华盛顿 (hua sheng dun, Washington) in MC vs. 华府 (hua fu, Washington) in TC. 6 For those who are interested in the dataset of this study, please contact the authors by the corresponding email: [email protected].


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7 We ended up with the following metonymic targets: GOVERNMENT (605 cases), EVENT (84 cases), PEOPLE (37 cases), ORGANIZATION/COMPANY (26 cases), TEAM (10 cases), and other categories (7 cases). 8 An Instance Based Learning algorithm, implemented in Python, proposed a topic, and the authors of this paper verified the propositions which are with low certainty. Instance Based Learning classifies unseen texts into the category of its most similar text in a manually annotated corpus. Similarity between 2 texts is measured by representing each text as a vector in a Euclidian space and taking the cosine of the

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angle between the 2 vectors. For the current task, a 3-Nearest Neighbor approach was used. A formal introduction on Instance Based Learning can be found in Chapter 8 of Mitchell (1997). The authors thank Tom Ruette for the topic-identification programming script. 9 All gpb capital names in the dataset are: London, Moscow, Paris, Washington and

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Brussels. Note that when Brussel refers to the capital of European Union in the context, it was classified into the group of gpb; when Brussel refers to the capital of Belgium, it was in the group of indif.

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10 The plot in Figure 2 is merely a rough indication, because there might be other predictors having an effect here, e.g. Topic. When we add the effect of Topic, we find that the effect of LangVar on the two sub-patterns is primarily there because of

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REFERENCES

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the business/economics news data as well as military news data.

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Allan, K. (2009). Metaphor and Metonymy: A Diachronic Approach. Wiley-Blackwell. Barcelona, A. (2000). On the plausibility of claiming a metonymic motivation for

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conceptual metaphor. In A. Barcelona (Ed.), Metaphor and Metonymy at the Crossroads: A Cognitive Perspective (pp. 31-58). Berlin & New York: Mouton de Gruyter. Barcelona, A. (2002). Clarifying and applying the notions of metaphor and metonymy within cognitive linguistics: An update. In R. Dirven & R. Pörings (Eds.), Metaphor and Metonymy in Comparison and Contrast (pp. 207-277). Berlin & New York: Mouton de Gruyter.


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Barcelona, A. (2003). Metonymy in Cognitive Linguistics: An analysis and a few modest proposals. In H. Cuyckens et al. (Eds.), Motivation in Language: Studies in Honor of Günter Radden (pp. 223-255). Amsterdam/Philadelphia: John Benjamins. Barcelona, A. (2004). Metonymy behind grammar: The motivation of the seemingly irregular grammatical behavior of English paragon names. In G, Radden & K.-U. Panther (Eds.), Studies in Linguistic Motivation (pp. 357-374). Berlin & New York: Mouton de Gruyter. Barnden, J. A. (2010). Metaphor and metonymy: Making their connections more

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English, German, Hungarian, and Croatian. In K.-U. Panther, L. Thornburg & A. Barcelona (Eds), Metonymy and Metaphor in Grammar (pp. 229-257). Amsterdam/Philadelphia: John Benjamins. Brdar-Szabó, R., & Brdar, M. (2003). Referential metonymy across languages: What can Cognitive Linguistics and Contrastive Linguistics learn from each other? International Journal of English Studies 3(2), 85-105. Deignan, A. (2005). A corpus linguistic perspective on the relationship between metonymy and metaphor. Style 39(1), 72-91. Fowler, R. (1991). Language in the News: Discourse and Ideology in the Press. London: Routledge.

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Dynamics and Interdisciplinary Interaction (pp. 163-189). Berlin & New York: Mouton de Gruyter. Geeraerts, D, Kristiansen, G. & Peirsman, Y. (2010). Advances in Cognitive Socialinguistics. Berlin & New York: Mouton de Gruyter. Goossens, L. (1990). Metaphtonymy: The interaction of metaphor and metonymy in expressions for linguistic action. Cognitive Linguistics 1(3), 323-340. Great Modern Chinese Dictionary (Xian Dai Han Yu Da Ci Dian). 2006. Shanghai: The Chinese Dictionary Press.

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Halverson, S. L. & Engene, J. O. (2010). Domains and dimensions in metonymy: A corpus-based study of Schengen and Maatricht. Metaphor and Symbol, 25, 1-18. Jing-Schmidt, Z. (2008). Much mouth much tongue: Chinese metonymies and metaphors of verbal behaviour. Cognitive Linguistics 19(2), 241-282. Kristiansen, G. (2008). Style-shifting and shifting styles: A socio-cognitive approach to lectal variation. In G. Kristiansen & R. Dirven (Eds.), Cognitive Sociolinguistics: Language Variation, Cultural Models, Social Systems (pp. 45-87). Berlin & New York: Mouton de Gruyter. Kristiansen, G., & Dirven, R. (Eds.). (2008). Cognitive Sociolinguistics: Language

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Variation, Cultural Models, Social Systems. Berlin & New York: Mouton de Gruyter. Kuo, S.-H., & Nakamura, M. (2005). Translation or transformation? A case study of language and ideology in the Taiwanese press. Discourse & Society 16(3), 393-417. Markert, K., & Nissim, M. (2003). Corpus-based metonymy analysis. Metaphor and Symbol 18(3), 175-188. Markert, K., & Nissim, M. (2006). Metonymic proper names: A corpus-based account. In A. Stefanowitsch & S. Th. Gries (Eds.), Corpus-based Approaches to Metaphor and Metonymy (pp. 152-174). Berlin/New York: Mouton de Gruyter. Markert, K., & Nissim, M. (2009). Data and models for metonymy resolution. Lang Resources & Evaluation 43, 123-138.

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Milić, G., & Vidaković, D. (2007). Referential metonymy of the type CAPITAL-FORGOVERNMENT in Croatian. In K. Kosecki (Ed.), Perspectives on Metonymy (pp.253270). Frankfurt: Peter Lang. Mitchell, T. (1997). Machine Learning. McGraw-Hill. Nissim, M., & Markert, K. (2003). Syntactic features and word similarity for supervised metonymy resolution. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (pp. 56-63), Sapporo, Japan.


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Panther, K.-U., & Thornburg, L. (2007). Metonymy. In: D. Geeraerts & H. Cuyckens (Eds.), The Oxford Handbook of Cognitive Linguistics (pp. 237-263). Oxford: Oxford University Press. Papafragou, A. (1996). Figurative language and the semantics-pragmatics distinction. Language and Literature 5, 179-193. Peirsman, Y. & Geeraerts, D. (2006). Metonymy as a Prototypical category. Cognitive Linguistics 17(3): 269-316. Pragglejaz Group. (2007). MIP: A method for identifying metaphorically used words in

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discourse. Metaphor and Symbol 22(1), 1-39. Radden, G., & Kövecses, Z. (1999). Towards a theory of metonymy. In K.-U. Panther & G. Radden (Eds.), Metonymy in Language and Thought (pp. 17-59). Amsterdam/ Philadelphia: John Benjamins. Speelman, D. & Geeraerts, D. (2009). Causes for causatives: The case of Dutch doen and laten. In: T. Sanders & E. Sweetser (Eds.), Causal Categories in Discourse and Cognition (pp. 173-204). Berlin/New York: Mouton de Gruyter. Steen, G., et al. (2010). A Method for Linguistic Metaphor Identification: From MIP to MIPVU. Amsterdam, John Benjamins.

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Stefanowitsch, A., & S. Th. Gries. (2006). Corpus-based Approaches to Metaphor and Metonymy. Berlin/New York: Mouton de Gruyter. Szmrecsanyi, B. (2006). Morphosyntactic Persistence in Spoken English: A Corpus Study at the Intersection of Variationist Sociolinguistics, Psycholinguistics, and Discourse Analysis. Berlin/New York: Mouton de Gruyter. Van Dijk, T.A. (1998). Ideology: A Multidisciplinary Approach. Newbury Park, CA: Sage.

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Lectal varieties

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Mainland Chinese Taiwan Chinese

Newspapers

Before manual

After manual

filtering

filtering

3572 articles

1633 articles

16658 instances

7857 instances

125 capitals

119 capitals

People's Daily, United Daily News, United Evening News, Economy Daily News, and Upaper


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Table 1 Overall information of the database before and after manual filtering

Factors

Predictor condition

Predicted effect

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CF

Anim=yes

favours Meto=yes

CF

Topic=politics

favours Meto=yes

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CF

Cap_group=self

disfavours Meto=yes

4

LF

LangVar=MC

favours or disfavours Meto=yes

5

DF

Juxta_meto=yes

favours Meto=yes

6

DF

Country=yes

disfavours Meto=yes

7

DF

PreMetoNo_cat=D

favours Meto=yes

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ID


DF

Title_meto_exact=yes

favours Meto=yes

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DF

Locus=Title

favours Meto=yes

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Table 2 Overview of the predictions we will test in the logistic regression analysis

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Note. CF, conceptual factor; LF, lectal factor; DF, discursive factor

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Predictors (in order of introduction in forward stepwise regression) (Intercept) Anim[T.no] Anim[T.yes] Juxta_meto[T.irrelevant] Juxta_meto[T.yes] Cap_group[T.gpb] Cap_group[T.indif] Cap_group[T.Asia] Cap_group[T.counterpart] Cap_group[T.warzone] PreMetoNo_inTitle_cat.ch =B PreMetoNo_inTitle_cat.ch =C PreMetoNo_inTitle_cat.ch=D Topic[T.business and economics] Topic[T.culture and living]

Estimates and p-values for the model (positive is pro ‘Meto=yes’) -6.92 (p < 0.001) 1.19 (p < 0.001) 7.98 (p < 0.001) 3.79 (p < 0.001) 10.97 (p < 0.001) 1.60 (p < 0.001) 1.98 (p < 0.001) -0.01 (p=0.980) 1.60 (p < 0.001) 0.17 (p=0.785) 0.87 (p < 0.001) 0.38 (p =0.005) 0.42 (p < 0.001) -1.28 (p < 0.001) -2.42 (p < 0.001)

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(p =0.064) (p = 0.151) (p =0.200) (p < 0.001) (p = 0.912) (p =0.956) (p < 0.001) (p < 0.001) (p =0.003) (p = 0.011) (p =0.035) (p = 0.009) (p = 0.463) (p < 0.001) (p = 0.174)

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-2.97 -1.31 -1.70 -1.95 0.03 -0.02 -2.95 0.98 0.53 0.81 0.96 1.83 -0.59 4.11 1.94

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Topic[T.military issues] Topic[T.other] Topic[T.science and education] Topic[T.social issues] Topic[T.sports] Country[T.dualrole] Country[T.yes] Locus[T.Title] LangVar[T.TC] Title_meto_exact[T.irrelevant] Title_meto_exact[T.yes] Anim[T.no]:Country[T.dualrole] Anim[T.yes]:Country[T.dualrole] Anim[T.no]:Country[T.yes] Anim[T.yes]:Country[T.yes]

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Table 3 Estimates for the logistic regression model for the data Note. We choose p=0.05 as the significance level. The baseline value for each variable

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is: Anim=irrelevant, Juxta_meto=no, Cap_group=self,

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PreMetoNo_inTitle_cat.ch=A (with Helmert coding), Topic=politics,

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Country=no, Locus=Mainbody, LangVar=MC, and Title_meto_exact=no.

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Model with main effects and two-way interactions

Number of instances

7857 (of which 769 "metonymic capital" and 7088 "non-metonymic capital")

Null deviance

5034.6 (on 7856 df)

Residual deviance

1429.7 (on 7827 df)[AIC is 1489.7]

Model chi squared

800.13 (on 29 df)

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Summary statistic

p