Retrieving Linguistic Information from a Corpus on the Example of Negation in Chinese

Abstract

The paper deals with corpus analysis of negation in Chinese, namely the negatives bù 不 and méi/ méiyǒu没/没有. The adverbs BU and MEI are two of the most frequent negatives in Chinese. The aim of this study is to present statistical data together with linguistics analysis. The results provide empirical evidence of discrepancy between “authentic” language data versus linguistic prescription with practical implications for second-language acquisition. The findings inter alia suggest a new approach to verb categorisation.

Full text

Acta Linguistica Asiatica, 9(2), 2019.
ISSN: 2232-3317, http://revije.ff.uni-lj.si/ala/
DOI: 10.4312/ala.9.2.103-115
RETRIEVING LINGUISTIC INFORMATION FROM A CORPUS ON THE EXAMPLE
OF NEGATION IN CHINESE
Ľuboš GAJDOŠ
Comenius University in Bratislava, Slovakia
lubos.gajdos@uniba.sk
Abstract
The paper deals with corpus analysis of negation in Chinese, namely the negatives bù 不 and
méi/méiyǒu 没/没有. The adverbs BU and MEI are two of the most frequent negatives in
Chinese. The aim of this study is to present statistical data together with linguistic analysis. The
results provide empirical evidence of discrepancy between “authentic” language data versus
linguistic prescription with practical implications for second-language acquisition. The findings
inter alia suggest a new approach to verb categorisation.
Keywords: Chinese language; corpus linguistics; quantitative description; negation; potential
complements
Povzetek
Članek obravnava korpusno analizo negacije v kitajščini, pri čemer se avtor osredotoča na
prislova bù 不 in méi/méiyǒu 没/没有, ki sta najpogostejši nikalnici v sodobnem kitajskem
jeziku. Namen prispevka je predstaviti statistične podatke v povezavi z jezikoslovno analizo.
Rezultati študije prinašajo empirične dokaze o neskladju med jezikovno rabo in jezikovnimi
normami, ta spoznanja pa je moč uporabiti tudi pri poučevanju kitajščine kot tujega jezika in za
premislek o drugačnem pristopu na kategorizacijo glagolov.
Ključne besede: kitajščina; korpusno jezikoslovje; kvantitativni opis; negacija; zmožnostna
dopolnila

104 Ľuboš GAJDOŠ
1 Introduction
Generally speaking, there are a number of negatives in modern Chinese.
1
In this article
only two negative adverbs, namely bù 不 and méi/ méiyǒu 没/没有
2
(hereafter referred
to as BU and MEI), are discussed. The Hanku corpus is used
3
as the primary source of
language material and statistical data. As the intention is to mainly use the corpus-
driven
4
approach to studying of negation, thus the previous linguistics research on this
topic is left aside.
Let us start with some basic queries:
5
[tag="AD" & word="不" ]
[tag="VV|AD" & word="没|没有"]
6
The results are 7371142 (9897.85 per million),
7
686352 (921.62 per million)
8
respectively. These numbers only tell that the token BU is approximately 10 times more
frequent than MEI. The difference is even more pronounced when searching in a
certain variety of Chinese, e.g. in the corpus of legal Chinese, the occurrence of BU is
45254 (6 281.56 per million), the occurrence of MEI 720 (99.94 per million). Let us take
a closer look at the tokens that collocate with these negatives. The following queries
should return collocates at the position 1 on the right side:
9
[tag="AD" & word="不"][]
[tag="AD"& word="没|没有"][]
1
For details see e.g. Liu (2004, pp. 253–258).
2
For the sake of simplicity, both negatives mei and meiyou are treated as two forms of one negative,
namely MEI. On the other hand, their collocative partners may differ because of e.g. prosodic factors.
3
See more in Gajdoš, Garabk and Benick (2016, pp. 53–65).
4
See more in Baker, Hardie and McEnery (2006, p. 49).
5
In this article, the Corpus Query Language (hereafter CQL) is used to search for collocations. With
CQL, complex criteria can be set to find one or many tokens. Criteria for each token must be
between a pair of square brackets [ ], e.g. [attribute="value"]. See more at
https://www.sketchengine.eu/documentation/cql-basics/
6
As there are more tags (e.g. VV = verbs, VE = YOU as the main verb, AD = adverbs) dedicated to
tokens mei 没 and meiyou 没有, it is rather difficult to accurately determine the value of the negative
MEI. Thus, I only use tags VV, AD in this article. For more details on the tagset see Fei (2000, pp. 4–
35).
7
Unless stated otherwise, frequencies are given in absolute occurrence in the Hanku corpus.
8
The occurrence of mei 没 is 342190 (459.49 per million) and 344162 (462.14 per million) for meiyou
没有.
9
The regular expressions may match the following patterns:
adverb bu + any token or
adverb mei/meyou + any token.
As it is rather difficult to identify the collocates at the position further to the right using only POS
tags, this topic will be left for future research. See e.g. Gajdoš (2018).

Retrieving Linguistic Information from a Corpus on the Example … 105
The results are summarised in the tables below.
10
Table 1: The most frequent POS at the position 1 (Corpus: web-zh)
# Query: word, [tag="AD" & word="不"][]
# Query: word, [tag="AD|VV" & word="没|没有"][]
不
No. of results: 7371142
没|没有
No. of results: 686352
tag
frequency
tag
frequency
AD VV
5092363
VV VV
326984
AD VA
827140
AD VV
193781
AD VC
695511
VV AD
35762
AD AD
444055
VV P
30138
AD P
155025
AD AD
16246
AD PU
25581
VV AS
15136
AD AS
19730
VV PU
9902
AD JJ
19525
VV CD
5771
AD PN
13121
VV PN
5350
AD NN
10044
VV VA
4975
AD BA
9276
VV BA
4870
AD VE
8798
VV NN
4449
AD LB
8553
VV DT
4223
AD CD
7535
AD VA
3511
AD SB
6815
AD P
3488
AD NR
5930
VV SB
2795
AD DT
4226
VV DEC
2660
AD DEC
3170
VV LB
1914
AD DEV
2380
AD SB
1541
AD SP
1987
VV DER
1460
AD LC
1744
AD BA
1334
AD M
1709
VV JJ
1209
AD MSP
1545
VV NR
1106
AD NT
1097
VV NT
959
AD OD
1032
AD CD
861
AD CC
1023
AD PN
691
AD CS
1021
AD VC
663
AD DEG
941
VV VC
526
AD DER
118
VV OD
499
AD ETC
88
VV LC
380
AD FW
45
AD VE
354
AD IJ
14
AD LB
338
10
The results are calculated using the NoSketch Engine UI – Node tags.

106 Ľuboš GAJDOŠ
The table indicates different collocability for the negative BU and MEI, e.g. the negative
BU exhibits a strong preference for copulas (here VC).
11
For practical reasons, only the
POS tags, which are more frequent than 1% of each group (here in bold), are included
in the analysis. The PU tag is also to be excluded from further analysis as it stands for
punctuation.
Table 2 shows 10 of the most frequent collocates for each negative. The results are
calculated using the NoSketch Engine UI – Node forms.
12
Table 2: The most frequent tokens at the position 1 (# Corpus: web-zh)
# Query: word, (meet [tag="VV|VA|VC|AD|P"]2:[tag="AD" & word="不"]-1 -1)
# Query: word, (meet [tag="VV|AD|P|AS"]2:[tag="AD|VV" & word="没|没有"]-1 -1)
不
No. of results: 7214094
没|没有
No. of results: 621555
word
frequency
word
frequency
是
693223
想到
56260
能
377529
看
14539
会
311133
能
13999
知道
288756
了
12223
要
227692
见
9843
存在
125328
在
9249
可
125212
说
9215
到
124252
想
9141
得
91523
看到
7189
敢
85127
用
6744
At first sight, it is surprising that the collocation MEI+ néng 能 is the third most frequent,
despite the fact that most grammars and textbooks deny this possibility.
13
Similar
findings may provide the impetus for further research which would take greater
11
The co-occurrence of MEI+VC is caused by the misspelling of the character shi in most cases, e.g.
mei shi 没是 instead of mei shi 没事.
12
To find collocative partners of both negatives, the operator meet is used. That means that the
corpus is search for the following patterns:
adverb BU + verbs (VV) or
adverb BU + adjectives (VA) or
adverb BU + copulas (VC) or
adverb BU + adverbs (AD) or
adverb BU + prepositions (P).
See more at https://www.sketchengine.eu/documentation/cql-meet-union/
13
There are some exceptions, e.g. Švarný and Uher (2014, p. 48) describe this phenomenon and Liu
(2004, p. 257) also suggests this possibility, however, they do not further elaborate this point.

Retrieving Linguistic Information from a Corpus on the Example … 107
account of actual language use. The “new” grammars or textbooks should be then
based on such research.
After searching the first hundred examples manually, it turns out that the co-
occurrence of some tokens with BU is higher than one would expect based on the
frequency of affirmation, e.g. liǎo 了(70691), zhù 住 (65400), qǐ 起 (40086) etc.,
furthermore, these verbs typically serve as so-called complements.
14
That means that
only tentative conclusion may be drawn from this evidence, nevertheless, it should play
a role when comparing the overall frequency of both negatives. I discuss this topic
further in the chapter Potential complements.
2 Potential complements
Let us return to the examples that have been mentioned in Chapter 1 and analyse them.
(1)
他
办
不/AD
了/VV
此事
Tā
bàn
bù/AD
liǎo/VV
cǐ shì
He
manage
BU-neg.
compl.
this matter
‘He cannot do this.’
(2)
杰克终于
忍
不/AD
住/VV
说了
jiékè zhōngyú
rěn
bù/AD
zhù/VV
shuō le
Jack finally
endure
BU-neg.
compl.
speak LE
‘Jack finally couldn't help saying it.’
(3)
这辈子房子
买
不/AD
起/VV
了
zhè bèizi fngzi
mǎi
bù/AD
qǐ/VV
le
this life house
buy
BU-neg.
compl.
LE
‘(One) cannot afford to buy a house for the entire life.’
(4)
对于双方不
能
不/AD
说/VV
duìyú shuāngfāng bù
néng
bù/AD
shuō/ VV
for to both sides not
able to
BU-neg.
speak
‘regarding (things that) both sides cannot but speak’
Randomly selected samples suggest that many examples may be considered as so-
called potential complements with the “morphological” structure VV + BU + VV while
14
See e.g. Yip (2009, pp. 234–241).

108 Ľuboš GAJDOŠ
the first morpheme (verb) is not equal to the third. The following query meets this
condition:
15
(meet (meet 1:[tag="VV"][tag="AD" & word="不"]-1 -1) 2:[tag="VV|VA"]-2 -2) &
1.word!=2.word
The examples below show that the regular expression does not always match the
desired pattern and therefore must be modified.
(5)
那个人呢叫李一，
知
不
知道/VV？
Nàgè rén ne, jiào Lǐ Yī,
zhī
bù
zhīdào
That person is Li Yi
know
BU-neg.
know
‘Do you know that that person is called Li Yi?’
(6)
您
能
不
能够/VV
再具体地跟我们讲一下？
Nn
néng
bù
nénggòu
zài jùtǐ de gēn wǒmen jiǎng yīxià?
You
able to
BU-neg.
able to
tell us more specifically
‘Can you tell us this again more specifically?’
(7)
一定程度上，不
能
不
说/VV
Yīdìng chéngdù shàng, bù
néng
bù
shuō/VV
to a certain extent, not
able
BU-neg.
speak
‘To a certain extent, one cannot but speak.’
(8)
一个
说
不
要/VV
Yīgè
shuō
bù
yào
one
say
BU-neg.
want
‘One says no.’
(9)
这些人
找
不
到/VV
工作
Zhèxiē rén
zhǎo
bù
dào/VV
gōngzuò
these people
find
BU-neg.
compl.
work
‘These people cannot find work.’
There is a point worth noting here as well – auxiliary verbs (e.g. modal verbs) must be
removed from the search pattern. As there is no dedicated tag for modal or auxiliary
15
This regular expression matches the following pattern:
verb2/adjective2 + BU + verb1 and verb1 ≠ verb2, the verb1 is KWIC (Key Word in Context).

Retrieving Linguistic Information from a Corpus on the Example … 109
verbs (except VE, VC), each of the verbs must be enumerated in the query with the
attribute "word".
16
A double negative must be excluded too. The refined query is:
17
(meet (meet (meet 1:[tag="VV"& word!= "要|能" "& word="(?i).{1,2}"]
[tag="AD" & word="不"]-1 -1) 2:[tag="VV|VA"& word!= "要|能"]-2 -
2)[word!="不"]-3 -3) & 1.word!=2.word
The following table shows the result. The overall frequency is 828224 (1112.13 per
million).
Table 3: The most frequent potential complements – the negative form (# Corpus: web-zh)
# Query: word,(meet (meet (meet 1:[tag="VV"& word!= "会|能|应该|该|必须|可以|可|应
当|可以|应|能|能够|必须|须|要|可能|会|需要|愿意|敢|该|需|知道" &
word="(?i).{1,2}"][tag="AD" & word="不"]-1 -1) 2:[tag="VV|VA"& word!= "会|能|应该|该|必
须|可以|可|应当|可以|应|能|能够|必须|须|要|可能|会|需要|愿意|敢|该|需|知道
"]-2 -2)[word!="不"]-3 -3) & 1.word!=2.word
word
Frequency
到
111457
了liǎo
65697
住
59793
起
36774
得dé
33857
出
31377
上
29568
开
22326
过
19412
见
12632
着zháo
10366
出来
10114
懂
9877
16
The query above contains only two of these verbs, the others are present here, e.g. 能|应该|该|
必须|可以|可|应当|应|能够|必须|须|要|可能|会|可|需要|愿意|敢|该|需 etc. The limit for
the length of the tokens is set to 1 or 2 by the expression: "word="(?i).{1,2}".
17
The regular expression means that the corpus is searched for the following pattern:
token (not BU) + verb2 (not 要 nor 能) + adverb BU + mono- or disyllabic verb1 (which is not 要
nor 能) and verb1 ≠ verb2. Only the verb1 is KWIC in the concordance and other tokens are used
as contextual filters. See more at https://www.sketchengine.eu/documentation/cql-meet-union/

110 Ľuboš GAJDOŠ
The result of the affirmative form might be achieved by the same query with only minor
modification:
18
(meet (meet (meet 1:[tag="VV"& word!= "要|能" "& word="(?i).{1,2}"]
[tag="DER"]-1 -1) 2:[tag="VV|VA"& word!= "要|能"]-2 -2)[word!="不"]-3 -3)
& 1.word!=2.word
The total frequency of 167822 (225.35 per million) clearly shows that the
occurrence of the affirmative form is far less frequent. This fact only validates the
previous assumption mentioned in the literature.
19
The following list contains a sample
of the most frequent verbs: 上 11598, 起10084, 到9769, 住 7 614, 出 7607, 出来,
3736, 见 2977 etc.
If we move back to the calculation of the overall frequency of BU, the value of the
negative form of potential complements (1112.13 per million) should be subtracted
from the total frequency, i.e. 8785.72 per million. Needless to say, these are only
approximate numbers and further research is required.
3 Verb collocates
The first chapter discusses the collocability of the negative BU and MEI. In this chapter,
I further explore this topic. When comparing the total frequency of BU vs. MEI, some
considerations should be taken into account, i.e. some verbs/adjectives collocate with
BU only, some registers use only a limited number of MEI etc.
After saving the results as a text file (from the NoSketchEngine UI), I proceed to
test the 2 lists
20
for the duplication
21
and calculate the average value of co-occurrence.
When comparing two lists for duplication in the spreadsheet program, there are many
tokens in the MEI list which are marked as they have no counterpart in the BU list. This
might cause surprise at first since one would expect only tokens from the BU list not
having a counterpart. The explanation is rather simple: (1) most of these tokens have
a disyllabic morphological structure (V+X), e.g. 找到, 看到 and cannot be paired with
their monosyllabic counterpart in the BU list by the spreadsheet program (e.g. 找, 看)
or (2) the frequency of the BU counterpart is below the lowest frequency of samples
(see footnote 13).
18
There is a dedicated tag for the 得 de-marker, i.e. DER.
19
See e.g. Liu (2004, p. 583).
20
Each list contains the 1000 most frequent verbs that collocate with BU and MEI.
21
This might be done in MS Excel, LibreOffice Calc or any spreadsheet program.

Retrieving Linguistic Information from a Corpus on the Example … 111
Table 4: The 10 most frequent verbs collocating with BU and MEI (# Corpus: web-zh)
# Query: word,(meet [tag="VV"]2:[tag="AD" & word="不"]-1 -1)
# Query: word,(meet 1:[tag="VV" & word="(?i).{1,2}"][tag="AD|VV" & word="没|没有"]-1 -1)
不
没|没有
word
Frequency
word
Frequency
能
377442
想到
56260
会
311129
看
14539
知道
288756
能
13999
要
227691
见
9843
存在
125328
说
9215
可
124875
想
9141
到
122930
看到
7189
得
91458
用
6086
敢
85108
去
5602
知
78854
来
5371
The results indicate that:
 From the list of the 1000 most frequent tokens (verbs) with the negative BU,
619 tokens collocate with MEI too, yet from the 100 most frequent tokens,
there are 69 of them; the rest are e.g. the following tokens: 知, 行, 愿, 愿意,
肯, 应, 信 etc. that co-occur with BU only;
 From the list of the 100 most frequent tokens (verbs) with the negative MEI,
a few preferably collocate with MEI, e.g. 发现, 料到, 必要, 开始, etc.;
22
 The lower the frequency of a token in the BU list, the less frequent it collocates
with both negatives;
 Generally speaking, the co-occurrence of the negative MEI with the same verb
is about 2.5-time less frequent as with the BU negative, however, statistical
data reveals great disparities between tokens (see table 5). That is to say that
verbs on the left side of the table collocate almost always with the negative
BU, on the other hand, verbs on the right side almost exclusively collocate
with the negative MEI.
22
This may be seen from the following comparison: the query [word="没|没有" &
tag="VV|AD"][word="发现"] with the frequency of 4542 (6.10 per million) and the query [word="
不" & tag="AD"][word="发现"] 62 (0.08 per million).

112 Ľuboš GAJDOŠ
Table 5: Collocability of verbs (# Corpus: web-zh)
Preference for BU
Preference for MEI
word
ratio
word
ratio
知道
1511,8
想到
0,005
存在
858,4
放松
0,182
会
781,7
看到
0,201
住
408,7
留下
0,277
可
325,2
出现
0,290
在
307,7
进入
0,314
起
301,4
选择
0,323
合
278,5
感觉
0,340
应该
236,3
受到
0,342
了
235,4
表现
0,385
4 Adjective and adverbs collocates
This chapter focuses on the collocability of adjectives and adverbs and the same
searching methods are used.
As for the adjectives, a brief look at the given statistical data (827140 or 1110.67 per
million vs. 8486 or 11.39 per million; see table 6) demonstrates that adjectives (almost)
always collocate with the negative BU. The exceptions here may be considered as
phrases.
Table 6: Collocability of adjectives (# Corpus: web-zh)
# Query: word,(meet [tag="VA"]2:[tag="AD" & word="不"]-1 -1)
# Query: word,(meet [tag="VA"]2:[tag="AD|VV" & word="没|没有"]-1 -1)
不
No. of results: 827140
没|没有
No. of results: 8486
word
Frequency
word
Frequency
好
71999
错
1978
美
58946
成功
884
多
46448
好气
812
够
34917
多
374
一样
28451
真正
329
大
23685
必要
257
美观
18118
好
251
高
14844
明确
205
容易
14831
成熟
195
小
13191
好好
180

Retrieving Linguistic Information from a Corpus on the Example … 113
The situation with regard to adverbs is a little different. While the results indicate a
strong tendency to the negative BU, yet both negatives may be used.
Table 7: Collocability of adverbs (# Corpus: web-zh)
# Query: word,(meet [tag="AD"]2:[tag="AD" & word="不"]-1 -1)
# Query: word,(meet [tag="AD"]2:[tag="AD|VV" & word="没|没有"]-1 -1)
不
No. of results: 444055
没|没有
No. of results: 52008
word
frequency
word
frequency
再
76724
那么
6480
太
39336
再
4637
一定
22521
这么
4062
就
19298
完全
2989
只
17114
多
2855
曾
13088
怎么
2316
单
12286
真正
1889
正
10410
不
1541
多
8623
太
988
怎么
8621
甚么
920
5 Conclusion
To begin with, statistical data given in this study should only be taken as exhibiting a
general tendency and not as a fully accurate description of “real” language. It should
also be pointed out that this paper only examines the occurrence of negatives at the
first position to the left of collocates. In this respect, new methods should be devised
for solving issues addressed here, e.g. the problem with the POS annotation and its
error rate which may significantly affect statistical data or the problem with identifying
the difference between the negative MEI and the verb yǒu 有 (with the tag VE) etc. This
leads us to the questions how to interpret the results in light of these points and what
valuable results this study brings.
Firstly, when comparing results of both negatives, it seems that some verbs
described as “auxiliary” or “modal” tend to collocate with the negative MEI more often
than stated by language prescription. On the other hand, empirical data support the
claim that adjectives only collocate with the negative BU. As for adverbs, there is still a
strong preference for BU, but because I do not consider adverbs as a “true” collocate
to negatives (rather as part of a bigger structure), this question should be explored in
future research.
Let us now move on to the negative MEI. There are many verbs that preferably
collocate with MEI rather than with BU. A closer look at the results reveals that their

114 Ľuboš GAJDOŠ
morphological structure is disyllabic and the left morpheme is often a so-called
“resultative complement” (jiéguǒ bǔyǔ 结果补语). This finding may imply that the
category of verbal aspect and tense
23
deserves closer attention. That means if MEI is
regarded as past time marker, these verbs are commonly used in past tense and the
present tense (with BU) may describe the situation as a condition or future tense. A
similar phenomenon is also observed in some Slavic languages, where the present and
preterite of perfective verbs fulfil these functions too (e.g. compare the present
perfective form “urobm” vs. the past perfective form “urobil” in Slovak). This suggests
that these verbs in Chinese might be treated as perfective. In order to fully explore this
topic, the marker le 了, as a counterpart to the negative MEI, should be included in an
comparative analysis. There is a very detailed, corpus-based study conducted on this
subject by Petrovčič (2009), Operator Le in Chinese worth noting here.
To conclude, the article shows how to use a corpus when searching for evidence of
some language phenomena. As for negation in Chinese, the paper only suggests a
different approach to this subject and additional research is needed.
References
Baker P., Hardie A., & McEnery, T. (2006). A Glossary of Corpus Linguistics. Edinburgh: Edinburgh
University Press.
Fei, X. (2000). The Part-Of-Speech Tagging Guidelines for the Penn Chinese Treebank (3.0).
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23
See also Petrovčič (2017, pp. 108–109).

Retrieving Linguistic Information from a Corpus on the Example … 115
Appendix: The Hanku tagset
24
Tag
English
Example
AD
adverb
也
AS
aspect particle
着
BA
preposition BA in ba-construction
把
CC
coordinating conjunction
和
CD
cardinal number
十五
CS
subordinating conjunction
如果
DEC
markers – nominalizer
吃的
DEG
genitive marker
他的
DER
resultative DE 得
说得
DEV
manner DE 地
公正地
DT
determiner
这
ETC
et cetera
等
FW
foreign word
ISBN
IJ
interjection
喂
JJ
other noun-modifier
女
LB
preposition BEI in long bei-construction
被
LC
localizer
上
M
measure word
个
MSP
other particle
所
NN
noun
记者
NR
proper noun
英语
NT
temporal noun
今年
OD
ordinal number
第三
ON
onomatopoeia
哈哈
P
preposition
从
PN
pronoun
我
PU
punctuation
。
SB
preposition BEI in short bei-construction
被
SP
sentence-final particle
了
VA
predicative adjective
大
VC
copula
是
VE
verb 有/没有/无 as the main verb
有
VV
verb
说
24
For details see Fei (2000, pp. 4–35).