Inapplicable Words…

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一掛佛珠 一捧沙 一針強心劑

hang2 行 yi1 hang2 liu3shu4 一行柳樹

pi1 批 yi1 pi1 huo4 一批貨

zhuo1 桌 yi1 zhuo1 cai4 一桌菜

hu4 戶 yi1 hu4 nong2min2 一戶農民

pi3 匹 yi1 pi3 bu4 一匹布

zu3 組 yi1 zu3 ren2yuan2 一組人員

4.1.4.4 Inapplicable Words

Hao4號 in Table 24 is a word which is used to mark number, rather than calculate the number of objects, for example, roads. Hao4號 is not like ben3本 in yi1 ben3

shu1

一本書 denoting the sailent perceptual properties of the associated nouns or

jian4

件 in yi1 jian4 yi4wai4一件意外

having the concept of multiplier 1. Thus, hao4

號 is neither a classifier nor a measure word. I thus give it an NA (not applicable) mark and exclude it from our analysis.

Table 24: NA (not applicable) Words in Huang et. al. (1997)‘Mandarin Daily

Dictionary of Chinese Classifiers’

hao4

號

‘

第一號道路

’

4.1.5 Gao and Malt (2009)

Gao and Malt (2009) provide a list which includes one hundred and twenty-six commonly recognized Mandarin Chinese classifiers. These one hundred and twenty-six Mandarin Chinese classifiers are collected from Chinese books, newspapers, dictionaries, and causal conversations between Malt and Gao and the other native Chinese speakers and their own knowledge of Chinese. Gao and Malt (2009) mention that six native speakers of Mandarin Chinese from Beijing (three graduate students at Lehigh University and three college-educated spouses of

‧

graduate students) were paid to check if these one hundred and twenty-six classifiers are commonly used in Mandarin Chinese.

Even if Malt and Gao think that these one hundred and twenty-six classifiers are very reliable and familiar to college-educated speakers of Mandarin Chinese because they tested these one hundred and twenty-six classifiers again and again, I am able to point out certain defects in the list of these one hundred and twenty-six classifiers. As mentioned above in Section 4.1, a Mandarin Chinese classifier categorization proposed by Malt and Gao also does not provide any precise classification to show how an ambiguous classifier should be treated as a classifier or as a measure word and also includes measure words in their classifier categorization. As a result, I will re-classify the one hundred and twenty-six classifiers into three portions and the analysis processes will be omitted because they are the same as the above sections.

4.1.5.1 Words Re-classified as Classifiers

According to my re-classification, seventy-one classifiers are presented below.

Table 25: 71 Words of Classifiers in Gao and Malt (2009)

ben3 本 yi1 ben3 shu1 一本書

jian1 間 yi1 jian1 shu1dian4 一間書店

chang3 場 yi1 chang3 yin1yue4ju4 一場音樂劇

ju4 具 yi1 ju4 shi1ti3 ㄧ具屍體

tiao2 條 yi1 tiao2 wei2jing1 一條圍巾

chu1 齣 yi1 chu1 ge1wu3ju4 一齣歌舞劇

ke1 棵 yi1 ke1 song1shu4 一棵松樹

ting3 挺 yi1 ting3 ji1qiang1 一挺機槍

‧

chu4 處 yi1 chu4 shang1kou3 一處傷口

ke1 顆 yi1 ke1 xi1gua1 一顆西瓜

tou2 頭 yi1 tou2 da4xiang4 一頭大象

chuang2 床 yi1 chuang2 mian2bei4 一床棉被

li4 粒 yi1 li4 hong2dou4 一粒紅豆

wan2 丸 yi1 wan2 yao4wan2 一丸藥丸

chuang2 幢 yi1 chuang2 lou2fang2 一幢樓房

liang4 輛 yi1 liang4 jing3che1 一輛警車

wei4 位 yi1 wei4 lao3shi1 一位老師

dao4 道 yi1 dao4 zhuan1qiang2 一道磚牆

mei2 枚 yi1 mei2 jiang3zhang1 一枚獎章

ming2 名 yi1 ming2 xue2sheng1 一名學生

zhang1 張 yi1 zhang1 chunag2 一張床

feng1 封 yi4 feng1xin4 一封信

pian1 篇 yi1 pian1 wen2zhang1 一篇文章

qu3 曲 yi1 qu3 liu2xing2ge1 一曲流行歌

zhou2 軸 yi1 zhou2 hua4 一軸畫

ge 個 yi1 ge ren2 一個人

ren4 任 yi1 ren4 zong3tong3 一任總統

zhu1 株 yi1 zhu1ying1hua1 一株櫻花

guan3 管 yi1 guan3 mao2bi3 一管毛筆

shan4 扇 yi1 shan4 men2 一扇門

zhuang1 樁 yi1 zhuang1 yi4wai4 一樁意外

ji4 劑 yi1 jie4 qiang2xin1ji4 一劑強心劑

sheng1 聲 yi1 sheng1 jian1jiao4 一聲尖叫

‧

4.1.5.2 Words Re-classified as Xc and Xm

Eighteen Xc and Xm are presented below after re-classifying the one hundred and

twenty-six classifiers.

dian3 點 dian3 點 c yi1 dian3 zhu1sha1ahi4 一點硃砂痣

jia1 家 m yi1 jia1 ao4zhou1ren2 一家澳洲人

‧

zhi1 支 m yi1 zhi1 chun2mao2sha1 一支純毛紗

zong1 宗 zong1 宗 c yi1 zong1 yi4wai4 一宗意外

zong1 宗 m yi1 zong1 huo4wu4 一宗貨物

4.1.5.3 Words Re-classified as Measure words

Thirty-seven measure words that should not be included in the classifier

categorization are listed below.

Table 27: 37 Words of Measure words in Gao and Malt (2009)

bian4 辮 yi1 bian4 da4suan4 一辮大蒜

liu3 綹 yi1 liu3 tou2fa3 一綹頭髮

tie4 帖 yi1 tie4 zhong1yao4 一帖中藥

cuo1 撮 yi1 cuo1 mao2fa3 一撮毛髮

ma3 碼 yi1 ma3 shi4 一碼事

tuan2 團 yi1 tuan2 shi4bing1 一團士兵

duan4 段 yi1 duan4 gan1zhe4 一段甘蔗

pie3 撇 yi1 pie3 hu2xu1 一撇鬍鬚

wei4 味 hun1cai4 wu3wei4 葷菜五味

dui4 對 yi1 dui4 fu1qi1 一對夫妻

qi2 期 za2zhi4 di4yi1qi2 雜誌第一期

wo1 窩 yi1 wo1 xiao3gou3 一窩小狗

dun4 頓 yi1 dun4 fa4 一頓飯

qiang1 腔 man3 qiang1 re4cheng2 滿腔熱誠

xiang4 項 xing2fa3 di4ti1xiang4 刑法第一項

gu3 股 yi1 gu3 xiang1qi4 一股香氣

quan1 圈 yi1 quan2 liu3shu4 一圈柳樹

ya2 牙 yi1 ya2 ju2zi 一牙橘子

gua4 掛 yi1 gua4 fo2zhu1 一掛佛珠

shen1 身 yi1 shen1 yi1shang 一身衣裳

zhang1 章 di4yi1zhang1 nei4rong2 第 一章內容

ji2 集 yi1bai3ji2 lian2xu4ju4 一百集連續劇

si1 絲 yi1 si1 rou4 一絲肉

zhen4 陣 yi1 zhen4 ren2chao2 一陣人潮

juan4 卷 za2zhi4 di4yi1juan4 雜誌第一卷

tang2 堂 yi1 tang2 jia1ju4 一堂傢具

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4.2 True Classifiers

According to the above re-classifications of Mandarin Chinese classifiers from Section 4.1.1 to Section 4.1.5 (Chao 1968, Erbaugh 1986, Hu 1993, Huang et al.

1997 and Malt and Gao 2009), five groups of the classifier portion, the Xc and Xm portion and the measure word portion are given. Moreover, five groups of the classifier¹² portion of Chao (1968), Erbaugh (1986), Hu (1993), Huang et al. (1997) and Gao and Malt (2009) are also obtained. In the following, I will carry out further investigations on the basis of the words in these five groups of classifiers. In Section 4.2.1, the intersection method and union method in mathematics will be used to find core classifiers and non-core classifiers in the five groups of classifiers. In Section 4.2.2, a questionnaire experiment on identifying classifiers is used to examine the possibility for non-core classifiers to become true classifiers. Finally, the ultimate goal is to offer a group of true classifiers.

4.2.1 Core Classifiers and Non-core Classifiers

The intersection method and union method in mathematics are adopted in order to find core classifiers and non-core classifiers. In the following, I will individually discuss core classifiers through use of the intersection method and non-core classifiers through use of the union method.

First, core classifiers are obtained through use of the intersection method. In

12

Classifier here refers to both the classifier portion and Xc portion that have been mentioned in

Table 7.

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mathematics, the intersection (denoted as ∩) of two sects A and B is the set that contains all of the elements of A that also belong to B (or equivalently, all of the elements of B that also belong to A), but no other elements. The figure of the intersection of two sets is as shown below. T. Givón (1986) mentions that the shaded area in Figure 5 represents members which display all two ‘characteristic’ properties.

They are ‘the most typical’ members of the category, i.e., the prototype of the category.

Figure 5: Intersection of Two Sets

Also T. Givón (1986) mentions that the area where three out of four properties intersect are still ‘fairly’ typical except the area where four out of four properties intersect when intersection of four sets. I thus infer that the most typical members, which are X out of X intersect (X stands for any number), and also that fairly typical members, which are X-1 out of X intersect (X stands for any number), can be called core members. As a result, core classifiers in this thesis will be found through the above inferences of core members consisting of most typical members and fairly typical members. Below two steps are used to find core classifiers. First, five groups of the classifier portion are intersected, and then the most typical classifiers are

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obtained. Second, four groups of the classifier portion are intersected, and then fairly typical classifiers are also obtained. To make a summary of the most typical classifiers and fairly typical classifiers, twenty-two core classifiers are represented as in Table 28. These twenty-two core classifiers are not only identified as classifiers by representative studies but also by my analysis. These twenty-two core classifiers are also shared by the five representative studies. The above dual certifications support

that these twenty-two core classifiers are indubitable true classifiers.

Table 28: 22 Core Classifiers

ba3 把 c jian4 件 pi1 匹 wei4 位 ben3 本 ke1 棵 pian4 片 c zhang1 張 ding3 頂 ke1 顆 shou3 首 zhi1 枝 gen1 根 kuai4 塊 c sao1 艘 zhi1 隻 ge 個 li4 粒 tiao2 條

jia4 架 liang4 輛 tou2 頭

Next, non-core classifiers are obtained through use of the union method. In set theory, the union (denoted as ∪) of a collection of sets is the set of all distinct elements in the collection as shown in Figure 6.

Figure 6: Union of Two Sets

Take a simple example for instance, A = {1, 2, 3, 4} and B = {2, 4, 5, 6}. Thus, A∪B = {1, 2, 3, 4, 2, 4, 5, 6} and subtract the reduplicated portion {2, 4} which is equal to A ∩ B. {1, 2, 3, 4, 5, 6} will be obtained.

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However, the shaded area in Figure 6 does not correspond to the condition of non-core classifiers. The condition of the non-core classifiers in this thesis is that the non-core classifiers must be in the position outside the core classifiers. Thus, the shaded area in Figure 6 has to subtract the A ∩ B portion which represents core classifiers position again. The mathematical formula following represents the the concept of the non-core classifiers, A∪B － 2 × A ∩ B which can be simplified as [ A － (A ∩ B) ] + [ B － (A ∩ B) ]. The shaded area in Figure 7 shows the portion of the least typical classifiers to which A∪B － 2 × A ∩ B refers.

Figure 7: Portion of A∪B － 2 × A ∩ B

In this thesis, a mathematical formula, [ A－(A ∩ B) ] + [ B－(A ∩ B) ] is adopted to find non-core classifiers in the five groups of classifier portion. Table 29 below shows that non-core classifiers which are obtained through use of the above mathematical formula.

Table 29: 90 Non-core Classifiers

ban1 班 feng1 封 mu4 幕 xian4 線 c

ban4 瓣 fu2 幅 pan2 盤 xing1 星

bi3 筆 gan3 桿 pian1 篇 yan3 眼

bing3 柄 guan3 管 qi2 畦 ye4 葉

bu4 部 c ji4 劑 qi3 起 yuan2 員

ce4 冊 ji4 記 qu3 曲 ze2 則

chang3 場 jia1 家 c que4 闋 zhan3 盞

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chu1 齣 jia4 駕 ren4 任 zhan4 站 chu4 處 jian1 間 shan4 扇 zhao1 招 chuang2 床 jie4 介 sheng1 聲 zheng4 幀 chuang2 幢 jie2 節 c suo1 梭 zhi1 只 dang3 檔 jing1 莖 suo3 所 zhi1 支 c dao4 道 ju4 句 tai2 台 c zhi3 紙 dian3 點 c ju4 具 tang4 趟 zhou2 軸 ding4 錠 juan3 卷 ti2 題 zhu1 株 dong4 棟 kou3 口 c ting3 挺 zhu4 柱 dong4 洞 long3 壟 tong1 通 zhu4 炷 du3 堵 lü3 縷 c wan1 彎 zhuang1 樁 duo3 朵 lun2 輪 c wan1 灣 zong1 宗 c fa1 發 mei2 枚 wan2 丸 zun1 尊 fang1 方 men2 門 c wei3 尾 zuo4 座 fen4 分 c mian4 面 xi2 席

fen4 份 c ming2 名 xi2 襲

According to identical norms to re-classify classifiers proposed by these representative studies, I find that these twenty-two core classifiers are shared by representative studies. Thus, these twenty-two core classifiers are definitely true classifiers. Non-core classifiers are not shared by representative studies, so these non-core classifiers are only classifiers, not true classifiers. However, I think that there is the possibility for these non-core classifiers to become true classifiers. Thus, through an obejective questionnaire experiment to double check the possibility for these non-core classifiers to become definitely true classifiers.

4.2.2 An Experiment on Classifier Identifications

In this section, a questionnaire experiment is adopted to investigate the degree of possibility by which the non-core classifiers could become definitely true classifiers.

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The methodology, results and discussion are provided below.

Methodology

Our subjects in this questionnaire experiment limit to have a linguistic knowledge because subjects need linguistic knowledge when they use linguistic-based tests to differentiate classifiers and measure words. In this experiment, subjects are twenty-six linguistics graduate school students in National Chengchi University.

They are all Taiwnese and Mandarin speakers and all have received formal linguistic training. The questionnaire experiment comprises two parts. Part One includes a brief introduction of using yi-multiplier, de-insertion and ge-substitution to distinguish classifiers from measure words and a pre-test of classifier identifications. Becasuse the numeral / adjectival stacking is an optional test, it is not included in the Part 1.

The pre-test contains twenty-six test items with eighteen core classifiers from Table 22 and eight measure words whose multipliers are definitely not 1. (Please refer to Appendix A for the details of the pre-test). Subjects were asked to do the pre-test after reading the introduction. No time limit was set for the introduction and the pre-test. In the pre-test, three options were offered for each test item. If subjects view the test item as a classifier, they are to circle option C which represents classifiers. If subjects view the test item as a measure word, they are to circle option M which represents measure words. Or, if it is possible for the test item to be both a classifier and a measure word, subjects are to circle option O which represents classifiers and

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measure words. If subjects circle option O, they are asked to write down their interpretations of the meaning of the classifiers and measure words. If the correctness in the pre-test has 92 % to 100 %, subjects will be requested to do the formal test.

Totoally twenty subjects are requested to do the formal test. Part Two is composed of ninety non-core classifiers that are obtained from Table 29. The details of the formal test are shown in Appendix B. There is also no time limit during the formal test and three options are also offered for each test item. Subjects are asked to carry out the formal test in the same way as the pre-test.

Results and Discussion

In the data analysis, I use percentages to represent the statistics. If a subject circles

C (classifiers) once, then the classifiers will be calculated once. If a subject circles M

(measure words) once, then the measure words will be calculated once. Also, if a subject circles O, O will be counted once. The percentage of three options in each test item adds to one hundred percent. Table 30 shows the statistics of the results from the formal test. In the following, two aspects from Table 30 will be discussed.

First, to discuss the relations between these three options from option O’s point of view: the option O is chosen twenty-four times. This result shows that the subjects have experienced some degree of confusion over these twenty-four items when differentiating these twenty-four items as classifies or measure words. The higher the percentage of the choice of the option O, the more uncertainty the subjects feel. For

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example, the percentage of option O for bi3筆 is 20 %. And the percentages of the occurrence of bi3筆

as a classifier and of bi3

筆

as a measure word are 40 % and

40%, respectively. We can thus infer that if the percentage of the occurrence of option O is high, then the percentages of the occurrence of one word as a classifier and of one word as a measure word are likely to be concordant. However, this inference is not absolute because there are some counter-examples. For example, the percentage of the occurrence of option O of chu4處 is 10% and the percentage of the occurrence of chu4處 as a classifier and of chu4處 as a measure word are 80%

and 10%, respectively, and the percentage of the occurrence of option O of dang3檔 is 5% and the percentage of the occurrence of dang3檔 as a classifier and of dang3 檔 as a measure word are 65% and 30%, respectively. Although the percentage of the occurrence of option O of chu4處

is higher than that of dang3

檔, the difference in the percentages of the occurrence of chu4處

as a classifier and of chu4

處

as a

measure word is larger than that for dang3檔.

Second, the relations among percentages of one word as a classifier and as a measure word and as option O show the following three phenomena. One phenomenon is that the percentage of one word as a measure word is over than as a classifier, such as bu4部

c, lü3

縷

c, pan2

盤

, tang4

趟and xing1星. Most subjects circled option M for bu4部

c, lü3

縷

c rather than option C. This implies that the

properties of bu4部

c and lü3

縷

c for being a measure word are more prominent than

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for being a classifier for most subjects. Next, pan2盤

and tang4

趟are strongly recognized as measure words by the subjects because the percentages of pan2盤

and

tang4

趟

as a measure word is 50% more than that of pan2

盤

and tang4

趟

as a

classifier. This implies that pan2盤

and tang4

趟 are measure words for most of subjects. Last is xing1星. Although xing1星 is rare in Taiwan Mandarin, most the subjects regard xing1星 as a measure word. This implies that the subjects tend to regard a new word as measure word. This further supports that measure words are an open set and are acceptable to innovations as proposed by Her and Hsieh (2010).

The second phenomenon is that the percentages of one word as a measure word and as a classifier are equal, such as qi2 畦. Unless the number of subjects is increased, it will be difficult to show if qi2畦 is a classifier or a measure word.

The last phenomenon is the percentages of one word as a measure word and as a classifier are quite similar, such as jia4駕

, long3

壟

and wan1

灣. Jia4駕

, long3

壟 and wan1灣 may not so common in subjects’ daily lives, or jia4駕

, long3

壟

and

wan1

灣, may be metaphorical usages in literature, so subjects may have difficulties in differentiating these words. Thus, the percentages of these words as a classifier and as a measure word are quite close.

Table 30: Statistics of Non-core Classifiers

No. Test Items Percentage of Test Items as a classifier (%)

Percentage of Test Item as a measure word (%)

Percentage of Test Item as both a classifier and a measure word (%)

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C mark is used to symbolize this ambiguous classifier functioning as a classifier rather than as a

measure word. C mark does not appear in the pre-test or the formal test.

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75

ze2 則 100 0 0

76

zhan3 盞 100 0 0

77

zhan4 站 60 40 0

78

zhao1 招 90 10 0

79

zheng4 幀 100 0 0

80

zhi1 只 100 0 0

81

zhi1 支 c 100 0 0

82

zhi3 紙 95 0 5

83

zhou2 軸 90 5 5

84

zhu1 株 90 10 0

85

zhu4 柱 95 5 0

86

zhu4 炷 95 5 0

87

zhuang1 樁 90 10 0

88

zong1 宗 100 0 0

89

zun1 尊 100 0 0

90

zuo4 座 100 0 0

After discussing the relations between the three options, Table 31 shows the percentage of test items as a classifier from high to low. In Table 31, I strictly stipulate that only test items with a 100% identification as a classifier are true classifiers because these test items are not only classifiers that mentioned in Section 4.2.1 but also objectively reconfirmed as classifiers by twenty subjects. The above dual certifications support that test items with a 100% identification as a classifier are true classifiers. There are a total of thirty-nine test items with a 100%

identification as a classifier in Table 31. These thirty-nine test items are thus definitely true classifiers under my stipulation. The remaining test items are classifiers, but they are not true classifiers because they violate my stipulation that only test items with a 100% identification as a classifier are true classifiers. Merely,

‧

the remaining test items are closer to true classifiers if the percentage of the test items as a classifier is higher. For example, test items with a score of 95% are closer to true classifiers than those with one of 90%. Then, test items with a score of 90%

the remaining test items are closer to true classifiers if the percentage of the test items as a classifier is higher. For example, test items with a score of 95% are closer to true classifiers than those with one of 90%. Then, test items with a score of 90%

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