Two approaches to generating Korean Numeral ClassifiersFrancis Bond,♠ Kyonghee Paik,♥ Jong-Bok Kim,♦ Jaehyung Yang ♣♠NICT Language Infrastructure Group, MASTAR Project♥Media Network Center, Waseda University♦
Kyung Hee University, ♣ Kangnam University
1

Introduction

In this paper, we compare two approaches to generating Korean numeral classiﬁers, both using semantic classes from CoreNet. In the ﬁrst approach, classiﬁers are assigned to semantic classes
by hand; in the second, the mapping is learned
from a corpus. Both approaches achieve comparable performance.

2Generation of Numeral ClassifiersFirst, let us brieﬂy explain the properties of numeral classiﬁers, focusing on Korean; then give analgorithm to generate multilingual classiﬁers.
2.1

What are Numeral Classifiers

Numeral classiﬁers are used for languages which
nouns cannot be directly modiﬁed by numerals.
However, quantiﬁer phrases can also function as
noun phrases on their own, with anaphoric or deictic reference, when what is being quantiﬁed isrecoverable from the context. For example (2) is
acceptable if the letters have already been referred
to, or are clearly visible. All the three languages
share this property.
(1)

to any quantiﬁers, only to numerals or theinterrogative [what]myech.
(3) 2-saram “2 people”(Numeral)(4) myetch-saram “some people” (Quantiﬁer)(5) myetch-mari “how many people” (Interrogative)
Numeral classiﬁers characteristically premodify
their target, linked by an adnominal case marker,
as in (6); or appear ‘ﬂoating’ as adverbial phrases,
typically to before the verb: (7) that governs theirtarget.
(6) :::::::
2-kwon-eui chaek-ul satta
2-cl-adn book-acc bought“I bought two books.”(7) chaek-ul :::::::
2-kwon(-ul) satta
book-acc 2-cl(-acc) bought“I bought two books.”
Sortal classiﬁers diﬀer from each other in therestrictions they place on their target. For example the classiﬁer -saram adds the restriction thatits target must be human. That is, it can only be
used to classify human referents.

[some background with books salient]
2.2(2) :::::::
2-kwon-ul satta
2-cl-acc buy“I bought two books”
Numeral classiﬁers are a subclass of nouns. The
main property distinguishing them from prototypical nouns is that they cannot be used bythemselves. Typically, they postﬁx to numerals,
forming a quantiﬁer phrase.
We will call all such combinations of a numeral/quantiﬁer/interrogative with a numeralclassiﬁer a numeral-classifier combination,and the noun phrase they quantify their target.
Languages diﬀer as to what the classiﬁers canfollow. Korean does not allow them to postﬁx

An Algorithm to Generate Numeral
ClassifiersThe basic algorithm we use is that of Bond andPaik (2000), an extension of the algorithm proposed by Sornlertlamvanich et al. (1994). The
algorithm is shown in Figure 1.
The algorithm can be used when a noun is amember of more than one semantic class or of no
semantic class. In the lexicon we used, nouns are,on average, members of 2 semantic classes. However, we assume that semantic classes are ordered
so that the most basic class comes ﬁrst. Duringcontextual processing, other semantic classes may
become more salient, in which case they will beused to select the default classiﬁer.

－ 276 －The algorithm can also handle the generationof classiﬁers that quantify coordinate nounphrases. These commonly appear in appositive
noun phrases such as ABC-to XYC-no 2-sha:::::“the two companies, ABC and XYZ”.
1. For a simple noun phrase(a) If the head noun has a default classiﬁer
in the lexicon:
use the noun’s default classiﬁer(b) Else if it exists, use the default classiﬁerof the head noun’s most salient semantic
class (the class’s default classiﬁer)(c) Else use the residual classiﬁer( -kae for Korean)2@

2. For a coordinate noun phrase
generate the classiﬁer for each noun phrase
use the most frequent classiﬁer
Figure 1: Algorithm to generate numeral classiﬁers

If a noun’s default classiﬁer is the same as thedefault classiﬁer for its semantic class, then there
is no need to list it in the lexicon. This makes thelexicon smaller and it is easier to add new entries.
Any display of the lexical item (such as for maintenance or if the lexicon is used as a human aid),
should automatically generate the classiﬁer fromthe semantic class.
We extend step (1b) in one way: if a semantic
class has no classiﬁer associated with it, then we
use the classiﬁer associated with its hypernym. If
the hypernym has no classiﬁer we continue up thehierarchy until a classiﬁer is found, or we reach theroot. This allows us to mark the classiﬁers evenmore eﬃciently. We can mark the upper level
node for 11111:human with -myong for example,
and only mark exceptions further down the tree.

3et al. (1997). We choose it because of its rich ontology and its wide coverage of Korean, Japaneseand Chinese.
The CoreNet consists of 2,937 conceptual nodes(semantic categories) with 12 depth levels and of51,172 senses for nouns, 5,290 for verbs, and 2,081
for adjectives in Korean. The ontology has several hierarchies of concepts: with both is-a andhas-a relationships. Words can be assigned to semantic classes anywhere in the hierarchy. Not all
semantic classes have words assigned to them.
Each record in the dictionary has index form,part-of-speech, sense number and list of associated pronunciation, a canonical form, semanticclasses. Each word can have up to ﬁve common
noun classes and ten proper noun classes. In thecase of usagi “rabbit”, there are two common
noun classes and no proper noun classes. The
semantic classes are listed in order of salience (asjudged by the dictionary compilers). Consider theentry for bae which has several entries, diﬀerentiated by their semantic class. One is bae “ship”
with the semantic class 11322912:ship and another is bae “nashi pear” with the semantic class11322531:fruit.

7@

4

Mapping Classifiers to the Ontology

In this section we discuss two methods to associate classiﬁers to semantic classes.
4.1

Rational: Introspective Method

The ﬁrst method is to associate classiﬁers witheach of the CoreNet 2,937 semantic classes byhand. This takes around two weeks from scratch
and was the same used by Paik and Bond (2001).
We show the most frequent numeral classiﬁers
for for Korean in Table 1
4.2

Empirical: Corpus-based Method

We used a POS tagged corpus of newspaper reports (provided by KAIST). First we identiﬁed
all sentences with numeral classiﬁer combinations(ncl):The CoreNet Ontology

We used the ontology provided by CoreNet: A
Korean-Japanese-Chinese Aligned Wordnet with
Shared Semantic Hierarchy (Choi and Bae, 2003;
Korterm, 2005). It is based on, and very similar
to the Goi-Taikei — A Japanese Lexicon Ikehara

1. ncl = num+ cl postcl?
where－ 277 －num is a number or interrogative word
(POS \nnc or string
myech “howmany”)

-ËclassifierNone-kae(-hyoi(-myong(-bangul (-jang(-dae(-keun(-mari(Other@2 )$)-É)"ÉÏÖê)
"É)
4@)
.»)
"$)

Referents classiﬁed
Uncountable referents
abstract/general objects
eventspeopleliquidﬂat objects
mechanic items/ furniture
incidents
animals
26 classiﬁers

No.
799
737
707
296
26
24
20
14
14
73%
29.5
27.1
26.1
10.9
1.0
0.9
0.7
0.5
0.5
2.7

Sample Semantic Class
111:agent
11:concrete
122125141:visit
11111:person
113112722:tear
1132221:paper
113228:machinery
122125211:contract
537:beast

Table 1: Korean Numeral Classiﬁers and associated Semantic Classes
cl is a numeral classifer (marked with \nbu)
postcl is a bound morpheme that can follow a classiﬁer. Currently we recognize
the following•
isan “more than”•
iha “less than”•chongdo “about”•
karyang “about”
this class is not distinguished by the tagger, they are all marked (\ncn)$ "É$!"
,É\f
"#É

We rejected any classiﬁers from a stop list ofmensural classiﬁers, dates and currency units.
We then search for the target. If the ncl is followed by the adnominal marker (\jcm) and thenext word is noun, we take the following noun
sequence, else we take the preceeding noun sequence. We take the ﬁnal noun in the noun sequence as the antecedent. An example is given in
(8).

¥%»

4@,Ä')ÉÚê

(8) ::::::::::
1 2000komputer-lul
12000-dae::::::::::
1-ten-thousand 2000-cl
kyoche-handa
linuks-roconvert-do
computer-acc linux-dat
Twelve thousand computers converted tolinux.
From 314,806 sentences we were able to extract 45,937 Sortal/Event classiﬁer tokens. Therewere 158 candidate classiﬁers: the most common
was the residual classiﬁer-kae with 12,690 instances, then-myong “human” with 7,730 instances and third-dae “machine” with 5,480

-É

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4@

4@ ,Ä')

5

UX;Q!~Ñ"

$ÁÖäahim

instances. 27 classiﬁers had only one occurrence,
more than half of them were tagging errors.
Up till here the approach is very similar tothat of (Sornlertlamvanich et al., 1994). We
then go beyond there to map the targets tosemantic classes. As the corpus is not sense
tagged, we looked up each target in CoreNet, andlisted those we could ﬁnd by semantic class. We
then hand checked that the semantic class wassuitable for the classiﬁer. This gave us a tableof (classiﬁer, target, semantic class) instances.
For example, for (8), the mapping is ( ,,11322823:computer). We use these to make theempirical mapping. Where the same semantic
class appears with multiple classiﬁers, they areordered by token frequency, so that the mostfrequently occurring classiﬁer will be generated.
This mapping is richer than the rational mapping, as it has information about speciﬁc words,
but far less comprehensive.

Evaluation and Discussion

The algorithm was tested on the same set of 90
sentences used by Paik and Bond (2001). We only
considered sentences overt targets classiﬁed by asortal classiﬁer. Noun phrases modiﬁed by groupclassiﬁers, such as -soku “pair” were not evaluated, as we reasoned that the presence of sucha classiﬁer would be marked in the input to thegenerator. We also did not consider the anaphoricuse of numeral classiﬁers.
In total, there were 90 noun phrases modiﬁedby a sortal classiﬁer. We assumed as input only
the target word itself, and looked up its semantic
class in CoreNet.

－ 278 －The results, with a breakdown of the errors, aresummarised in Table 2. Correct stands for exact
match, acceptable for a diﬀerent register (for example, we generated :: -myeong although :::: saram was used in the test set). Incorrect means
we generated a diﬀerent classiﬁer than that in thetest corpus. Overgenerated means we generated
a classiﬁer where one was used in Japanese but
not in Korean.
The method based on intuition did better on
this test set, mainly because of its wider cover.
The corpus based method should do better on indomain data, we are currently constructing a new
test set to conﬁrm this. Another way of improving
the corpus based method would be to generalize
upward as well as downward, this is a topic forfurther work.

-É

ResultCorrect
Acceptable
Incorrect
Over Generated
Total

Rational% No.
62%
566%
513%
1219%
17100%
90""Ä

Empirical% No.
54%
493%
323%
2119%
17100%
90

Table 2: Results of applying the algorithm
Our classiﬁer mapping is dwarfed by the detailed work of Hwang et al. (2008), who give amore precise mapping of classiﬁer to semantic
class using the Korean WordNet (KorLex). However, currently they do not have any frequency
information, so have no way of selecting which of
the possible classiﬁers should be used for a given
noun. Guo and Zhong (2005) give a highly accurate method of selecting classiﬁers that uses manymore context features. We agree that this gives
better immediate results. However, our ultimate
goal is to ﬁrst do word sense disambiguation using
richer context features, and then use the appropriate semantic class with our algorithm. Thisshould allow us to back-oﬀ for words not seen before with a classiﬁer.

6of the time using a hand mapping and only 52%of the time using a mapping based on a domain
speciﬁc corpus. The algorithm uses the ontology
provided by CoreNet, and shows how accurately
semantic classes can predict numeral classiﬁers forthe nouns they subsume.
Acknowledgments
The authors thank KAIST and Key-Sun Choifor providing the corpus, and their overall support.

References
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Conclusion

In this paper we presented an algorithm to generate Korean numeral classiﬁers using a rich. It wasshown to select the correct sortal classiﬁer 72%－ 279 －