Using WordNet to predict numeral classifiers in Chinese and JapaneseHazel Mok Shu Wen, Gao Huini Eshley and Francis Bond
Linguistics and Multilingual Studies
Nanyang Technological University
haze0004@e.ntu.edu.sg, gaoh0004@e.ntu.edu.sg, bond@ieee.org

Abstract
Most nouns must be modified by a numeralclassifier combination when quantified inclassifier languages like Chinese and Japanese. In this paper, we present a method togenerate numeral classifiers using Chineseand Japanese WordNets. We assign synsets
from WordNet to each classifier by hand anduse a modified algorithm to generate sortal
classifiers based on semantic hierarchies. We
obtained a generation score of 78.80% for
Chinese and 89.84% for Japanese.

1

Introduction

Classifiers have long been an interest for many
linguists. In many Asian languages like Japaneseand Chinese, nouns often require numeral classifiers when they are quantified (Bond & Paik,
2000; Downing, 1996). This contrasts with English where count nouns can be modified by anumeral. In English, it is acceptable to say “twobooks”. In classifier languages, it is obligatory to
use a numeral-classifier pairing, as in (1) and (2).
(1) Japanese:2 冊 の本
2-satsu-nohon
2-CL-ADN book“2 books”(2) Chinese: 两本书liǎng běn
shū
2
CL
book
“2 books”
Numeral classifier languages typically lackplural markings. Greenberg (1972) has suggested
that a classifier in these languages functions likea plural suffix in languages that require plural
markings (cited in Downing, 1996). Hundius andKӧlver (1983) argue that a classifier establishes
immediate reference to individual objects.
The type of classifier used depends on thesemantic features of the noun referent (Zhang,2007). Some of these semantic categories in-clude animacy and shape. For example, 只 zhǐ iscommonly used as the classifier for counting“animals” in Chinese. However, there are also
instances when nouns with similar properties use
different classifiers (Guo and Zhong, 2005). For
instance, 条 tiáo, which is used for long and thinobjects like “ropes”, is also used as the classifierfor “snakes”.
The Japanese distribution of classifiers is dependent on their referent classes: animates, concrete inanimates and abstract inanimates (Downing, 1996).
Bond and Paik (2000) identified five major
types of classifiers that have different properties
depending on the context. These are sortal, event,mensural, group and taxonomic.
Sortal classifiers are defined as those whichclassify the kind of noun they count (e.g. 辆liàng, the classifier for “vehicle”). Mensuralclassifiers measure some property of the object
denoted by the noun they modify (e.g. 米 mǐ
“metre”). Event classifiers are used to count
events (e.g. 次 cì “time”). Group classifiers referto a set of individuals belonging to the type denoted by the noun (e.g. 组 zǔ “group”). Taxonomic classifiers exert a generic interpretation of
the noun phrase that they modify (e.g. 种 zhǒng
“kind”).
Understanding the classifier systems of theselanguages can help us to appreciate how the languages cover the hierarchy of meaning with theuse of classifiers. In addition, it will provide us
with some insight on how speakers of these languages view the world.
Classifier systems are usually very complex.
They are also one of the more difficult aspects ofgrammar to acquire. Even native speakers may
have difficulty using some of them. Furthermore,classifiers are often poorly translated. The wrong
classifier may be used or left out altogether.
Hence, we hope that this study can help to im-prove the accuracy and efficiency of machinetranslation. The results of this study can also
benefit learners of Japanese and Chinese byhelping them retrieve the appropriate classifier
when forming noun phrases.
This paper is structured as follows. Section 2
gives a brief overview of work that has been
conducted in this area thus far. It also introduces
the resources that we will be using for this study.
Section 3 documents the methodology. Then, we
present the results in Section 4 and discuss thesignificance of the results and the limitations
faced in Section 5.

2

Background

There has been much more work done on analyzing classifiers than in generating them in natural
language processing. One important study investigating the generation of classifiers in Thai was
carried out by Sornlertlamvanich, Pantachat &Meknavin (1994). The authors proposed an algorithm for matching an appropriate classifier witha noun. Their study involved obtaining nounclassifier pairs from a tagged, word-segmented
corpus. From the pattern of noun-classifier collocations, they determined the best representativeclassifier for each noun and semantic class.
However, they did not include a detailed evaluation of the accuracy of their algorithm.
Bond and Paik (2000, 2001) presented a modified algorithm based on Sornlertlamvanich etal‟s (1994) work. This modified algorithm wasused for associating classifiers with semantic
classes in Japanese and Korean. It is able to handle nouns which belong to more than one semantic class. It does this by organizing the semantic
classes according to the noun referent‟s most frequent use. The general idea is to assign the default classifier of the most typical semantic classto the noun.
Resources
There are 145,000 synsets for different parts ofspeech (nouns, adjectives, verbs, adverbs) in the
Princeton WordNet of English v3.0 (PWN: Fellbaum 1998). The structure of WordNet allows
one to see the relationship between words suchas hypernyms (superordinates) and hyponyms(subordinates). It is often used for work in natural language processing.
The Japanese Wordnet (JWN: Isahara et al2008), contains about 57,238 synsets based on
the same lexical arrangement as PWN. Thismeans that lexical units in the Japanese Wordnet
were arranged according to their hierarchicalconnections among words as well. However, theJapanese and English wordnets are not a direct
copy of each other; for instance, there are Japanese synsets that are not found in the English
wordnet and vice versa due to the uniqueness ofboth languages (Isahara et al., 2009). One example is the concept of “rice”. Japanese makes a
distinction between 米 kome “rice” and 御 飯
gohan “cooked rice”. This distinction is notmade in English, and therefore the English
wordnet does not include a separate entry for thetwo senses.
The Wordnet used for Chinese is a bilingual
Chinese-English Wordnet (CWN: Xu, Gao, Pan,Qu and Huang, 2008). It is a bilingual lexical
database, which also uses the semantic hierarchy
from WN. This Chinese-English Wordnet has
more than 150,000 Chinese words. Each Chinesesynset is linked to an English synset, which allows for useful cross-language information retrieval.
We used a 38,000 sentence Japanese-EnglishChinese corpus, the NICT Multilingual corpus,(Zhang, Uchimoto, Ma and Isahara, 2005) basedon the Kyoto text corpus. The corpus was createdusing Japanese sentences from Mainichi Newspaper and manually translated into Chinese andEnglish.

3

Methodology

This section documents the steps taken in the
study.
3.1

Categorisation of classifiers

We extracted 228 Japanese classifiers and 264
Mandarin Chinese numeral classifiers from thecorpus. This was done by extracting anythingtagged as classifier for part-of-speech (POS). For
Japanese, we pulled out every word that was
tagged with meishi-setsubi-jousuushi “nounsuffix-classifier”. For Chinese, q.* was the POS
for classifier.
These were sorted into the following categories: sortal, mensural, date and time, currency
and not classifier. Sortal and mensural classifiers
were defined as mentioned before. Date and timeclassifiers measure the span of days and time
periods (such as 年 nián “year” and 秒 miǎo “second”). Currency classifiers are used to refer to a
country‟s currency (such as 美元 měiyuán“American dollar”). Lastly, nouns which had
been paired with a numeral, but were in fact notclassifiers, were removed (such as 三页 sānyè“three pages” and 两餐 liǎngcān “two meals”).
3.2

Hand annotation of corpus

Distribution pattern

Exampleclassifier-no-noun

2匹の犬(NUM)+CL no (NOUN)+

2-hiki-no-inu“2 of the dogs”

noun-no-classifier

犬の２匹(NOUN)+ no (NUM)+CL

inu-no-2-hiki“2 dogs”

noun-ga/wo/mo/waclassifier(NOUN)+ ga|wo|mo|wa(NUM)+CL

犬が/を/も/は/２匹
inu-ga/wo/mo/wa-2-hiki“dogs, 2”

Table 1. Distribution pattern for JapaneseDistribution pattern:(DET)? (NUM)+ CL1 (NOUN)+ 2
Table 2. Distribution pattern for Chinese
55 sortal classifiers were identified for Japaneseand we extracted sentences containing nounphrases that are modified by those sortal classifiers. We did the same for Chinese. Chinese had
more classifiers with 136 sortal classifiers identified in the previous step. Classifiers that appeared more than 100 times had their counts reduced. We used distribution patterns of classifiers and nouns to retrieve sentences with the numeral-classifier combination and the nounphrases. These distribution patterns were identified using language dependent patterns. Tables 1
and 2 show the distribution patterns identified forJapanese and Chinese respectively.
The distribution patterns that we identified
were able to retrieve many correct matches with
the numeral-classifier combination and target
noun phrase. However, there were some instances when the noun phrase identified was incomplete, as shown in (3) below.

1

2

There can only be one classifier in an expression.
DET: Determiner, NUM: Numeral, CL: Classifier, ?: 0 or
1, +: 1 or more(3) 三千七百sānqiānqībǎi
3700

名
míng
CL

会员
huìyuán
member几
普通市民
jípǔtōngshìmín
andcitizen“3700 members of the party and citizens.”
In (3), the target noun phrase picked out by
the regular expression was “members of the party”. However, the entire target noun phrase
should be “members of the party and citizens”.
In such instances, we had to redefine the correctboundaries for the full noun phrase.
After retrieving the matches made by the regular expression, we tagged the classifiers to thetarget noun phrases by hand and marked theboundaries for both classifier and target nounphrase. We also marked the type of relationshipthey had: sortal, mensural, event, group, anaphoric, non-classifier and other. In cases where
the target noun phrase was present in the sentence but was syntactically distant from the numeral classifier, as in (4), the relationship was
marked as anaphoric.
(4) 苦情は毎月
kujouTwa
mai-getsu
complaints
wa
every-month
平均 600件
heikin 600-ken1
average 600-CL

に
ni
ni

前年
zen-nenlast-year

より
yori
than

約
yaku
about

2000件
2000-ken
2000-CL増zouincrease

上る
noboru,add upのnono18320件を摘発した
18320-ken2
wotekihatsushi-ta
18,320-CL
wo
expose-PST“The number of complaints is as many as 600
per month on average and the police wrote
tickets for 18, 320 cases this year, up about 2,000
from last year.”In the example, the target of the first 件 ken,aclassifier used for things like “cases” or“matters”, is 苦情 kujou “complaint”. Therefore,
the classifier was tagged to 苦情 kujou“complaint” with a sortal relationship. However,for the second ken, although the referent 苦情
kujou “complaint” is still in the sentence, it is
not in the same clause as the classifier, therefore,
for this classifier, it was tagged as anaphoric.
Anaphoric targets share a sortal relationshipwith the classifier that modifies the noun phrase.
When the target is anaphoric, and therelationship between the target and classifier isunder „other‟, the set will be tagged as otherinstead of anaphoric.
Any instance of synecdoche was tagged as„other‟. One instance of synecdoche was foundfor 名 míng (one of the classifiers for “people”),
as shown in (5) below:
(5) 六
名
自民党
liù
míng zìmíndǎng
6
CL
Liberal Democratic Party“6 members of the Liberal Democratic Party”
In this example, the numeral-classifier paircounts the number of party members and not the
number of political parties.
Numeral-classifier combinations that were being used in an ordinal sense were tagged as „not‟.
In addition, noun phrases with very abstract referents like hope and courage were also tagged as„not‟ as they were considered uncountable.
3.3

Assignment of synsets to classifiersThen, we associated synsets from Japanese andChinese Wordnet to each of these classifiers byhand. We looked up the semantic class for eachtarget noun phrase and checked if it was suitable
for the classifier. We also checked how high inthe semantic hierarchy the use of the classifier
could extend to. Table 3 illustrates how we assigned synsets to 个 gè (general classifier) and只 zhǐ (animal classifier) based on the semantichierarchy shown in Figure 1.

Classifier
个 gè

Usage
general只 zhǐanimal条 tiáofish
snake
equine匹 pǐSynsets+00001740-n-00015388-n+00015388-n-02374249-n-02512053-n-01726692-n+02512053-n+01726692-n+02374149-n

Table 3. Assignment of synsets to classifiers______________________________________________________________________________
Figure 1. Semantic hierarchy in Chinese Wordnet
In both Chinese and Japanese, there is a general classifier that can be used for any entity
when there is no specific classifier (个 gè for
Chinese and 個 ko for Japanese). While 个 gè
in Chinese can be used to count both “humans”
and “objects”, 個 ko in Japanese cannot be
used for “humans”.
Since 个 gè is considered a general classifier,
we assigned the semantic class of entity_n_1
to it. A + symbol added in front of the synset signifies that all synsets below entity_n_1 will
share the same classifier, 个 gè. We also removed
animal_n_1 from it because animals are usually not counted with this classifier. The - symbol added in front of the synset signifies that thesynset animal_n_1 does not share the sameclassifier.
只 zhǐ is the classifier used to count “animal”.
We assigned animal_n_1 to it. We also removed
fish_n_1,
snake_n_1,
equine_n_1 from it. These animals are notcounted with the default animal classifier in Chinese. “Fish” and “snakes” are counted with 条

tiáo and “horses” and “mules” are counted with匹 pǐ. There were quite a number of nouns that
did not have an entry in both the Chinese andJapanese Wordnets. Thus, we had to add thesenouns into the Wordnets. Due to time constraints,
we only added nouns which occurred very frequently in the extracted noun phrases.
3.4

Generation of classifiers

We ran the annotated data through a program
such that it picked out the head noun from a nounphrase (6). This is achieved by having the program go through the noun phrase and match it toa synset in the Wordnet, with the assumption thatnoun phrases are right-headed.
Based on example (6), the head noun that is
retrieved from the noun phrase is オオカミ ookami “wolf”. The Chinese example in (7) showsthat the head noun extracted and matched with asynset is 飞机 fēijī “aircraft”.

(6) 灰色hai-irogray-colour“gray wolf”

オオカミ
ookamiwolf(7) 轻型
飞机
qīngxíng
fēijī
light
aircraft“light aircraft”
We used the modified algorithm by Bond andPaik (2000) to generate the classifier. Based on
this algorithm, the classifier most closely associated to the head noun in terms of semantic class
was generated (Figure 2). All hyponyms underthat synset will be counted with the same classifier unless a specific classifier has been marked
for it.
Based on intuition, the algorithm will selectthe classifier marked on the closest possible hypernym. For example, the synset ante-___________________________________________For a noun phrase:(a) find its synset(b) find all hypernyms and see if any are selected for by classifiers(c) select the classifier from the synset withthe highest similarity_______________________________________
Figure 2. Algorithm to generate a classifier
To analyse noun phrases with more than one
sense, we used an algorithm shown in Figure 3.
This algorithm identifies the semantic class ofthe noun based on the classifier used.
From the Japanese Wordnet, シイル shiiru“seal” has at least two senses:
a. seal_n_9 marine mammal (subset of
mammal)b. seal_n_5 a stamp affixed to a
document (subset of stamp)
Since the classifier 匹 hiki is tagged to thesemantic class of animal, the sense of シイル
shiiru “seal” implied in the example is that of thesynset seal_n_9, a marine mammal which is ahyponym of animal.
___________________________________________For a noun phrase:(a) Identify the semantic class of the headnoun based on the classifier present
e.g. シイル ２匹 買いました
shiiru 2-hiki kaimashita
seal 2-CL buy-PST“I have two seals”
Figure 3. Algorithm to analyse a noun phrase
In this generation stage, we only looked atnouns with unique synsets that had been identified in the analysis stage. Lastly, we tested thepredictions made using Wordnet to check theaccuracy of the predictions.

lope_n_1 is compatible with 只 zhǐ, which is4

the classifier for “animals” and 头 tóu, which isthe classifier for certain types of animals like“pigs”, “cattle”, “elephants” and “livestock”.

JapaneseClassifier

Usage

Count

Since 头 tóu is associated with bovid_n_1,
which is the hypernym of antelope_n_1, we人nin“person”

122

件

ken

69

台

dai

社

sha

“(abstract) matters/cases”“vehicles”; “machines”“companies”;“shrines”

select 头
tóu as the classifier for antelope_n_1.

Results

58
47

本

hon

枚 mai

個 ko
点 ten
棟 tou

long, thin objects e.g.
“roads/ties/pencils”
thin, flat objects e.g.
“papers/photographs/plates”
general measure word;“military units”“pieces of a set”;“goods/items”“buildings/apartments”

45

Table 6. Results of hand annotation

40

Table 6 summarises the results of the hand annotation for Japanese and Chinese. The total
number of classifier phrases in Chinese is much
more than in Japanese. This is because Chineseclassifiers can also appear with determinatives

30
21
15

“houses”
15
戸 ko
Table 4. Ten most frequent Japanese classifiers
ChineseUsage
Classifier

Count

家 jiā

“families/businesses”

名 míng“people”个 gè“people/objects”

场 chǎng
件 jiàn“events e.g. exams/sporting events”“things/clothes”

位 wèi“people” (honorific)

87“long and thin thingse.g.
snakes/rivers/ropes”“lives”“vehicles”

82

条

tiáo

辆 liàng

112

like 这 zhè “this”, not just with numerals.
As shown in Table 6, the number of sortal
classifiers in Japanese that share a sortal relationship with the target is less than half. This is much
lower than we expected.
For Chinese, there were 1,906 noun phrases
modified by a sortal classifier. This was slightly
more than half of the total number of extracted
sentences. Classifiers tagged as „other‟ and „not‟were mostly being used in an ordinal sense or
had uncountable abstract noun referents.

107

Scores111
95

Correctly
analysed
Total

93

80

“flat objects/things
74
with flat surfaces e.g.
zhāngbeds, paper”“votes”“phrases/lines of70
句 jùverse/sayings”
Table 5. Ten most frequent Chinese classifiers张

Tables 4 and 5 show the ten most frequent classifiers in Japanese and Chinese respectively.

Japanese
(J)
592
133
61
7
407
142
1400

Chinese
(C)
1906
113
26
41
267
921
3274

Total
(J)
405

%
Total
(C)
(C)
79.37 1312

100

528

100

1653

Correctly
89.84
116
78.80 223
generated
Total
100
129
100
283
Table 7. Analysis and generation scores
Table 7 presents the results of our evaluation.
The analysis score tells us how often wematch a noun‟s semantic class and the generation
score tells us how often we correctly generate aclassifier. A generated classifier is judged to becorrect if it exactly matched the original classifier used in the annotated corpus. For Japanese, theanalysis score is 76.33% and the generation scoreis higher at 89.84%. The analysis score is slightly
lower by 3.04% than that for Chinese. However,
the generation score is higher by 11.04% ascompared to Chinese.

5Classifier
type
SortalAnaphoric
Event
Group
Other
Nonclassifier
Total

%
(J)
76.33

Discussion

For this study, we only considered classifiers thatshare a sortal relationship with the noun phrase
they modify. Noun phrases that are modified byclassifiers that share a group, event, mensural
relationship were not included in the evaluation.
Similarly, noun phrases in which the target isanaphoric were also not included.
Based on the results of the hand annotation,
most sortal classifiers often have an anaphoricuse. The anaphoric target can either be in thesame sentence but different clause or in a different sentence.
Overall, the evaluation of both algorithms is
satisfactory. For Chinese, we were able to analyse correctly 79.37% or 1312 noun phrases. By
using the default classifier assigned to each semantic class, we were able to generate correctly
78.80% or 223 classifiers.
For Japanese, we were able to correctly analyse 76.33% or 405 noun phrases and generate
89.84% or 108 classifiers.
One of the issues we faced in this study is the
problem of dealing with synecdoche, particularly
for Chinese. In example (5), given in Section 3.2,
we saw the classifier 名 míng being used tocount the number of members of the politicalparty and not the number of political parties.
Based on our mapping of classifiers to semantic class, the “Liberal Democratic Party” would
belong to the semantic class of organization
which uses a different classifier 个 gè. However,this type of synecdoche will not be captured using the current method of analysis.
For Japanese, a large number of classifiers thatwere tagged with having “other” relationshipwith the targets were in fact functioning as ordinal classifiers. These were often preceded by an
ordinal prefix (8) or followed by the ordinal suffix 目 me (9).
(8)

第一棟 の 旅館dai-1-dou no ryokan
ORD-1-CL no hotel“the first hotel”(9) 二回目の 優勝
ni-kai-me no yuushou
2-CL-ORD no victory“the second victory”
One of the limitations of this study is the coverage of Wordnet. During our assignment ofsynsets to classifiers, we found that approximately 20% of our target noun phrases were not represented in CWN. For instance, 球队 qiú duì “ateam for ball sports” was not included. Although
it had synsets for 棒球队 bàngqiúduì “baseballteam” (baseball_team_n_1) and 篮 球 队lánqiúduì “basketball team”, (basketball_team_n_1), it did not have a genericterm to refer to a team that played ball sports.
For the Japanese study too, there are some nouns
that are not yet represented in JWN, for example
大手 oote “major company” or チッシュウ tis-shuu “tissues”. In addition, the lexicon does notinclude proper nouns like names of companies
like 三 菱 電 機 mitsubishi denki “Mitsubishi
Electric Corporation”. In a similar manner, anoun may be present in Wordnet but is missing
the correct sense. One example is 白紙 hakushi“blank paper”. Although this noun is represented
in JWN, the sense given is that of “fresh start”.
The lack of a corresponding noun or sense in thelexicon may have affected the evaluation scores.
Similarly, some nouns which were represented
in CWN were also missing other senses. For instance, 车 chē has two senses. The first sense is“car” and the second sense is “rook”, a type ofchess piece. When we looked up 车 chē “car”
CWN presented us with rook_n_2. This senseis the subset of corvine bird.
In this case, the noun is being represented by
the wrong synset and one of its sense “car” wasalso missing. Although we assigned the synset
for vehicle_n_1 to the classifier 辆 liàng (theclassifier for “vehicles”), it was not able to generate this classifier when it encountered 车 chē“car” in the test sentences.
In addition, since the Chinese sentences weretranslated from Japanese sentences from theMainichi Newspaper, there were also a few Japanese loanwords which were not represented in
Chinese Wordnet.

Some of these include 榻榻

米 tàtàmǐ “tatami” and 横岗 hénggāng “yokozuna”. This could also account for the lower generation scores obtained for Chinese as compared toJapanese. Although CWN covers more synsets,
its coverage of common senses appears to beslightly worse than JWN.
Some nouns can also be used with more than
one classifier. For instance, in Japanese, 住宅
juutaku “residence” can be used with classifiers
軒 ken, 個 ko, or 棟 tou, all three being classifiers for houses. In the corpus, there were instances of all three classifiers being used to quantify 住宅 juutaku “residence”. The choice of
which classifier to use is up to the individual‟spersonal preference. Hence, it is difficult to predict the correct classifier for cases like this.
Another issue that may have contributed to thegeneration error is the problem of fixed expressions, particularly for Chinese. This was often
seen with the classifier 口 kǒu, which is used forcounting things with mouths. It is commonlyused to count the number of people in a family orhousehold, as shown in (10).
(10) 一家 五口yījiā wǔkǒua family5CL“a family of five.”人
rén
personAlthough (10) shows that 口 kǒu can be used
to count “person”, this classifier is generally used
this way only when it follows 家 jiā “family”. 个
gè is the more common classifier to use whencounting “person”. Such fixed expressions cannot be properly analysed with our current methods of analysis and will require special processing.
Shape, size and animacy are some factors thatplay a part in selecting the correct classifier (Allan, 1977). For instance, 张 zhāng is used tocount flat objects or things with a flat surface.
Some examples include tables, stools, papers,
newspapers and beds. However, the wordnets do
not contain such information about shape or sizeof the nouns. Hence, some world knowledge isstill required in order to predict the right classifier for a target noun phrase.
In order to further research on Japanese andChinese classifiers, we release the following datafor both languages under the creative-common
attribution license (CC-by): (i) the table of classifier phrase + antecedent noun phrase pairs with
their disambiguated synset (ii) the table of whichsynsets are classified by which classifiers (Figure1). It is available from the Japanese Wordnet
page: http://nlpwww.nict.go.jp/wn-ja/. We arealso feeding back information on missing senses
to the respective Wordnet projects.

6Conclusion

In this paper, we presented an algorithm to generate numeral classifiers based on semantic hierarchies present in wordnets. For Chinese, it was
shown to select the correct sortal classifier
78.80% of the time. We believe that this scorecan be raised with improvements to Chinese
Wordnet. For Japanese, it was shown to selectthe correct sortal classifier 89.84% of the time.
At the present moment, the wordnets do not provide a full coverage of all the nouns in the world.
In addition, there are factors that may guide thechoice of selection, making a purely taxonomichierarchy inadequate. This study has shown thatthe selection of a classifier based only on a taxonomic hierarchy may not be accurate all the time
because semantic attributes of the noun are also

important. Future studies can work to improve on
the coverage of wordnet and also perhaps expand
the wordnet in terms of linking semantic attributes. World knowledge is also required in order
to select the most suitable classifier.
Acknowledgements
We wish to acknowledge the funding support for thisproject from Nanyang Technological University under
the Undergraduate Research Experience on Campus(URECA) programme. We thank NICT for granting
permission to use the NICT multi-lingual corpus.

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