Deep Processing of Korean and the Development of
the Korean Resource Grammar∗
Jong-Bok Kim & Jaehyung Yang, & Sanghoun Song, & Francis Bond
Kyung Hee U., & Kangnam U., U. of Washington, Nanyang Technological U.
jongbok@khu.ac.kr, jhyang@kangnam.ac.kr, sanghoun@uw.edu, bond@ieee.org
Linguistic Research 28.3: 635–672

Abstract
The Korean Resource Grammar (KRG) is a broad-coverage, linguistically precise, and open-source grammar of Korean that has been developedsince 2003. This paper reports how the KRG, aiming at deep processing of
Korean, has gone through two main developing phases, in particular from alinguistically-oriented deep grammar to a wider-coverage with grammar customization. The development of the KRG in these two phases, also participating in the international collaboration project DELPH-IN (Deep Language
Processing in HPSG), has resulted in promising directions for the improvement of deep parsing as well as generation methods in a more systematic way
so that it can be geared toward practical NLP applications such as machinetranslation.

Key words: Korean Resource Grammar, deep processing, parsing, generation,
HPSG, Minimal Recursion Semantics

1

Deep Linguistic Processing

For the past decade or more, linguistically oriented methods and statistical or machine learning approaches to NLP have often been perceived as incompatible oreven competing paradigms. While shallow and probabilistic processing techniques(yielding simple parsed trees and meaning representations) have produced useful
results in many classes of applications, they have not met the full range of needs
for NLP, particularly where precise interpretation is important, or where the variety∗

Numerous people have contributed to the development of the KRG (Korean Resource Grammar).
Among others, we thank the following people: Daren Appling, Emily M. Bender, Chung Chan,
Incheol Choi, Jae-Woong Choe, Sae-Youn Cho, Dan Flickinger, Yasunari Harada, Valia Kordoni,
Eric Nichols, Stephan Oepen, Byong-Rae Ryu, Ivan Sag, Peter Sells, and Kiyotaka Uchimoto, EunJung Yoo, among others. Our thanks also go to the anonymous reviewers of this journal. This work
was supported by the National Research Foundation of Korea (NRF-2011-32A-A00066).

1

of linguistic expression is large relative to the amount of training data available (cf.
Uszkoreit 2002). On the other hand, due to advances in algorithm efficiency andrich deep processing resources, deep processing approaches to NLP (yielding richand detailed syntactic and semantic representations) have only recently achieved
broad enough grammatical coverage and sufficient processing efficiency to allow
the use of precise linguistic grammars in real-world applications (cf. Baldwin etal. 2007).1
This need for deep language processing has also given motivations for the international collaboration DELPH-IN (Deep Linguistic Processing with HPSG). Aiming to provide an open-source collection for deep linguistic processing of humanlanguage within the HPSG framework, this joint collaboration tries to work together to promote the robustness of deep processing for natural language, focusing
on areas such as (i) robustness, disambiguation, and specificity of deep processing
with HPSG, (ii) the application of HPSG deep processing to information extraction, and (iii) multilingual grammar engineering (cf. Kordino and Neu 2005). As
positive results, there have been several successful resource grammars developed,
including the ERG (English Resource Grammar, Flickinger 2000), JACY (Siegel
and Bender 2002 for Japanese), resource grammars for languages like German(GG) and other Indo-European languages.
The KRG (Korean Resource Grammar) has also been developed under thisopen-source NLP consortium since 2003, aiming to build a computational opensource grammar of Korean (Kim and Yang 2004b). The grammar has gone throughtwo main development phrases. At the first phase, the KRG focused on linguistic
phenomena, while in the second phase, the grammar has tried to adopt the grammar customization system using the LinGO Grammar Matrix (Bender et al. 2002)
This direction not only improves the parsing efficiency but also adds generation capacity, prerequisite to its NLP applications (cf. Song et al. 2010). This paper aims
to present some core properties of the deep computational grammar of Korean. In
particular, we discuss how the grammar has been improved in two main phases,
providing future directions for deep linguistic processing, which plays key roles inpractical applications.

2

Main Architecture for Deep Processing

The KRG has been constructed within the following open-source infrastructure,and is released at http://krg.khu.ac.kr under the MIT license2
1

As noted here, we use the term ‘deep processing’ as a term for methods with full grammaticaland semantic analyses while ‘shallow processing’ as a term referring to methods using a diminishedlevel of linguistic precision. See Baldwin et al. (2007).
2
The licence’s philosophy is to give people the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies so long as the MIT copyright notice and this permission notice
is included.

2

HPSG: The grammatical framework the KRG is couched upon is the framework of HPSG (Sag et al. 2003 and references therein) and most of the main
grammatical analyses follow those set forth in the KPSG (Korean Phrase Structure Grammar, Kim 2004). HPSG is a constraint-based, non-derivational, lexicalist
approach to grammatical theory that seeks to model human languages as systems
of constraints on typed feature structures. In particular, the grammar adopts themechanism of type hierarchy in which every linguistic sign is typed with appropriate constraints and hierarchically organized. The characteristic of such typed
feature structure formalisms facilitates the extension of grammar in a systematicand efficient way, resulting in linguistically precise and theoretically motivated descriptions of languages including Korean. The grammar HPSG is thus well suited
to the task of multilingual development of broad coverage grammars.
In addition,for semantic representations the KRG uses a flat semantic formalism Minimal Recursion Semantics (MRS) (Copestake et al. 2005). MRS offers
an interface between syntax and semantics using feature structures. In MRS, themeaning of a given expression is represented as a flat bag of elementary predications (EPs), combining naturally with typed feature structures and allowing structures underspecified for scopal information.
LKB: The basic tool for grammar writing, testing, and processing the KRG
is the LKB (Linguistic Knowledge Building) system developed by researchers at
CSLI, Stanford University (Copestake 2002, Copestake et al. 2005). The LKB
system is a grammar and lexicon development environment for use with constraintbased linguistic formalisms such as HPSG. The LKB system, freely available with
open source (http://ling.stanford.edu) has a compiler for TFS (typed feature structure) grammars and parser and generator that maps from strings to meaning andvice versa. One strong advantage of this system is that a type-feature based grammar like HPSG can be easily encoded in the system as we will see in what follows.
The Grammar Matrix: Our system also uses the Grammar Matrix customization system for the development of the KRG. The matrix system is an open source
tool designed for the rapid development of precision-based grammars, within the
HPSG and MRS formalism (Bender et al. 2002). This framework plays an important role in describing a HPSG/MRS-based grammar in a short-period of time, andimproving it continuously.
Testing and Parsing Tools: The main software from the DELPH-IN collaboration is packaged with the so-called LOGON infrastructure. The system contains software packages, such as LKB for parsing and generation, PET for parsing(Callmeier 2000), and a management tool [incr tsdb()] (Oepen 2001). The platform[incr tsdb()] produces detailed diagnostic reports and complex multi-dimensional
comparisons between alternative system. There are also several grammars includedin the LOGON such as the ERG, JACY, resource grammars for Spanish, Greek,
French, German, and so forth. Along with these, some pre-compiled versions of3

preprocessing or experimental tools are packaged in the LOGON distribution.3
The architecture of the KRG incorporated in this LOGON system can thus berepresented in the following figure.

Figure 1: KRG in the LOGON system

3

Korean Resource Grammar: the First Phase

3.1

Basic Picture of the Grammar

Aiming at the development of a deep processing grammar for Korean within thesketched LOGON architecture, the KRG has been developed since 2003 and eversince gone through two main phrases (cf. Kim and Yang 2003, Kim and Yang
2004b). The main components of the KRG, in particular at the beginning phase,
can be illustrated in Figure 2.
As shown in Figure 2, once a Korean source sentence is given as an input,
the system as the preprocess level performs a morphological analysis and thenperforms its syntactic and semantic analyses. The syntactic and semantic parsing
steps are built upon the basic grammatical components of the KRG such as grammar rules, inflection rules, lexical rules, type definitions, and lexicon. The typedescriptions include matrix.tdl for general principles, korean.tdl for language particular rules, types-lex.tdl for lexical types, types-ph.tdl for phrasal types, etc. The
3

wiki.delph-in.net/moin/LogonTop

4

Figure 2: Main Components of the KRG
characteristic of such typed feature structure formalisms facilitates the extensionof grammar in a systematic and efficient way, resulting in linguistically precise andtheoretically motivated descriptions of languages including Korean (see Copestake(2002)).
As noted, the grammar of the KRG basically follows the KPSG developed
within the HPSG formalism (Sag et al. 2003, Kim and Sells 2008). The KRG
starts with the following type hierarchy system in which every linguistic sign istyped with appropriate constraints and hierarchically organized (cf. Kim 2004):
(1)

sign
V
hhhh VVVVVVV
h
h
h
VVVV
h
h
h
VVVV
hhhh
VV
hhhh
lex-stsyn-stMVVM
qqq MMVMVMVMVVVVV
qqq MMMMMqqqq
V
MMM VVVV
MMM
qq
qq
VVV
M
M
qqq
qqq
ph-ex
verbal nominal adverbial...
lex-exzIIuuzz IIII
uu
z
u
II
uu
zz
II
uu
zz
I
z
uu
z
z hd-comp-ph ...
word hd-lex-ex
hd-subj-ph

The elements in lex-st type, forming the basic components of the lexicon, are built
from lexical processes such as lexical rules and type definitions.4 Parts of theseelements will be realized as word to function as syntactic elements since thoseinflected as word can occur in syntax. The type word has subtypes such as nword, v-word, and adv-word. As noted earlier, though all nominal elements can be4

For the detailed lexicon structure, refer to Kim and Yang (2004b).

5

projected into n-word, only v-free can be mapped (or pumped up) to v-word since
not fully inflected stems like mek-ess ‘eat-PST’ cannot appear in syntax.5 One
important constraint that word in the KRG observes is the following Argument
Realization Constraint (cf. Kim and Sells 2008):(2)

Argument Realization Constraint (ARC):SYN | VAL
word → 
ARG-ST

"

SUBJ
COMPS

A
B

A ⊕ B

#




The constraint means the elements in the ARG-ST will be realized as SUBJ andCOMPS in syntax (cf. Kim 2004, Kim and Sells 2008). This in turn means that
ARG-ST is sensitive to the word-level only, while the syntactic features SUBJ andCOMPS are relevant at syntax. For example, when the lexeme ilk- ‘read’ specified only with the ARG-ST information is fully inflected as ilk-ess-ta ‘read-PASTDECL’, its two arguments will be realized as SUBJ and COMPS as represented in
(3):6
(3)



v-tr


ORTH hilk-i
 →ARG-ST hNP, NPiv-wordORTH hilk-ess-tai










POS verb






HEAD V +








STATIVITY − 
SYN 


"
#






VAL SUBJ h 1 iCOMPS h 2 iARG-ST h 1 NP, 2 NPi

The morphological processing of adding the past tense ess and the mood marking
ta adds the HEAD features (e.g., POS, VFORM, and STATIVITY).7
Once we have word elements in syntax, these elements will be combined withother syntactic elements. It is the type ph-ex that places restrictions on the combination of syntactic elements including word. This in turn means that the subtypesof ph-ex will tell us what kind of well-formed phrases is available in the language.
The following are the grammar rules that license the subtypes of the type ph-ex:(4)Grammar Rules:

5

The type v-free thus means a fully-inflected verb that can appear in syntax. See Kim and Yang
(2004b) for a detailed description of the verbal inflection system in Korean.
6
In the lexicon, the ARG-ST value is not encoded since its information is predicted from the typev-tr.
7
The space does not allow us to explicate the morphological system of the grammar. This systemsegments words into sequences of morphemes with POS tags and morphological information. In the
KRG, it is in fact the type definitions on each morphological elements that play more crucial roles informing morphological elements and incurring relevant grammatical information.

6

a.

Head-Subject Rule:hi
XP[hd-subj-ph] → 1 , H SUBJ h 1 ib.

Head-Complement Rule: hiXP[hd-comp-ph] → 1 , H COMPS h..., 1 , ...ic.

Head-Modifier Rule:hi
XP[hd-mod-ph] → MOD h 1 i , 1 H

These main grammar rules can license major phrases in the language. The HeadSubject Rule, generating a hd-subj-ph, allows a VP to combine with its subject.
The Head-Complement Rule ensures a head to combine with one of its COMPS(complements) elements, forming a hd-comp-ph. The Head-Modifier Rule allows
a head to form a well-formed phrase with an adverbial element that modifies thehead, resulting in hd-mod-ph. These grammar rules, interacting with general principles and lexical information, can produce well-formed Korean sentences like thefollowing:
(5)

S


#



POS verb HEAD 1
VFORM fin 





SUBJ h i

hd-subj-ph"

COMPS hiq]]]]]]]]]]]]]
qqq
q
q
VP
q


qqq
q
q
hd-mod-phqqqHEAD 1
2 NP


22


22
SUBJ h 2 i 
22
COMPS hi22]22kkk ]]]]]]]]]]]]
k
k
22
kk
4 VP
kkk
22
kkk


k
22
hd-comp-ph
Adv
HEAD 1imotun haksayng-tul-i hMOD h 4 i
SUBJ h 2 i 
COMPS hiq]]]]]]]]]]]]]
qqq
q
q
V
qqq

q
q
q
HEAD 1qqq

3 NP
SUBJh 2 iCOMPS h 3 ippali

ARG - ST h 2 , 3 isakwa-lul7

mek-ess-ta







The sentence meaning ‘All students ate apples quickly’ is generated observing all
the grammatical constraints declared in the KRG.8 The verb mek-ess-ta ‘eat-PSTDECL’ selects two arguments, each of which is realized as SUBJ and COMPS
according to the ARC. The head verb then combines with its COMPS sakwa-lul‘apple-ACC’, forming a well-formed hd-comp-ph in accordance with the HeadComplement Rule. The resulting VP then is modified by an adverb ppalli ‘quickly’
according to the Head-MOD rule. This phrase eventually combines with the subject motun haksayng-tul-i ‘all student-PL-NOM’, forming a hd-subj-ph licensed by
the Head-Subject Rule. Each phrasal combination also observes high-class constraints such as the Head-Feature Principle.
As illustrated here, the KRG generates binary syntactic structures, observing
tightly interacting grammatical constraints. These constraints are lexical, phrasal,
as well as constructional.

3.2

Semantics

In representing the semantics, the KRG adopts the MRS representations designed
to enable semantic composition using the unification of typed feature structures.
The system allows us to produce for each phrase or sentence a description of themeaning representation in a systematic and compositional way. For example, thefollowing is the meaning representation we obtain for the sentence in (5) (see Kim
2006a for details):
(6)



"

#



LTOP h1
HOOK INDEX e1













PRED all q rel
PRED eat v rel










PRED prpstn m rel LBL h3
 LBL h7, ARG0 i,LBL h1
, ARG0 e1













ARG1 iRESTR h2* MARG h9+
ARG2 j
BODY h11


RELS





PRED exist q rel




 

LBL h9
 PRED apple n rel 
PRED
student
n
rel





, LBL h8
LBL h10, ARG0 j
 






RESTR h4
 ARG0 j
ARG0 i
BODY h12










* qeq
+
qeq




HCONS 1HARG h2 , HARG h4
LARG h10

LARG h8

As noted here, the three main semantic features for each sentence are RELS,
HOOK, and HCONS. The feature RELS is a bag of elementary predications (EP)8

Such a sentence can induce scope ambiguities which can be captured in the KRG, but is notdiscussed in detail here because of the space limit. See Kim (2006b) for further discussion.

8

whose value is a relation. Each relation has at least three features LBL (label),PRED, and ARG0. The feature HOOK is a group of distinguished externally visible attributes of the atomic predications in RELS and includes the attributes LTOP,
INDEX, and XARG. The final feature HCONS represents set of handle constraints
on scoping. This value can be resolved in such a way that the quantifiers ‘float in’
wherever there is a ‘space’ left by a qeq (equality modulo quantifiers) constraint
through the attributes LARG and HARG. When words with such semantic information combine with other syntactic (elements of syn-st) expressions, the meaning
composition occurs in accordance with the Semantic Compositional Principles ensuring that in any well formed phrase structure, the mother’s RELS value is thesum of the RELS values of the daughters (See Copestake et al. 2005 for details).

3.3

Main Phenomena Covered

3.3.1

Sentence Internal Scrambling

One welcoming consequence of the KRG with this basic picture is that it can capture sentence internal scrambling facts in a straightforward way, one of the mostcomplicated facts in the SOV types of language. For example, the sentence in (7a)with five syntactic elements can induce 24 (4!) different scrambling possibilities.
The language allows all the following word order possibilities as variations of (7a):(7) a.

mayil
John-ihaksayng-tul-eykey yenge-lul
kaluchiessta
everyday John-TOP student-PL-DAT English-ACC taught‘John taught English to students everyday.

b.

John-i yenge-lul mayil Tom-eykey kaluchi-ess-ta

c.

John-i mayil Tom-eykey yenge-lul kaluchi-ess-ta.

d.

John-i Tom-eykey mayil yenge-lul kaluchi-ess-ta.

e.

John-i Tom-eykey yenge-lul mayil kaluchi-ess-ta.

f.

...

The grammar rules in (4) allow only binary structures. This is possible due to thefact that the Head-Complement Rule allows a head to combine with just one of thecomplements and that the Head-Subject Rule allows the subject to be combined atany stage.
It is needless to say that a more desirable grammar would be one that can capture all such scrambling possibilities within minimal processing load. The KRG
grammar we sketched here requires no additional mechanism (i.e. movement operations) to allow such diverse word order possibilities. The grammar rules given in(4) can license and parse all these with no additional mechanisms such as complex
movement processes.

93.3.2

Case Phenomena

As is well-known, Korean has a rich inflectional system for verbal and nominal
elements. Nominal affixes, encoding various grammatical functions including case,are optional but are generated in tightly restricted ordering.9 Once we have theright generation of nominal elements with case information, the next issue is how
argument-selecting heads and grammar rules contribute their case information tonominal elements.10 Let us consider intriguing case alternation phenomena:(8) a.

b.

John-inokcha-ka/*lul
coh-taJohn-NOM green.tea-NOM/*ACC like-DECL‘John is fond of green tea.’
John-inokcha-lul/*ka
coh-a
hanta
John-NOM green.tea-ACC/*NOM like-COMP do‘John likes green tea.’

The stative verb coh-ta assigns NOM to the object. However, when it combineswith the auxiliary verb hanta, the object can get ACC.
The KRG adopts the lexeme-based lexicon where all the verbal lexemes will
minimally have the following information:
(9)



v-lxm
ORTH hilk-i



h
i h
i

ARG-ST hNP GCASE vcase , NP GCASE vcase iThis means that any element in the ARG-ST gets the value vcase as its GCASE(grammatical case) value: the vcase value can be either nom or acc in syntax.
The elements in the ARG-ST will, in accordance with a realization constraint, berealized as SUBJ and COMPS in syntax as indicated in the following:
(10)



hilk-ess-ta ‘read-PST-DECL’ i






HEAD | POS verb


"
#





SUBJ h 1 i 
SYN



VAL


2i
COMPS
h


h
i
h
i

ARG-ST h 1 NP GCASE vcase , 2 NP GCASE vcase iWith this declarative verb ilk-ess-ta ‘read-PST-DECL’, the SUBJ element can be
nom whereas the COMPS can be acc, but not the other grammatical case value as
noted in (11):
(11)

9
10

John-i/*ul
chayk-ul/*i
ilk-ess-ta
John-NOM/ACC book-ACC/NOM read-PAST-DECL‘John read a book.’

See Kim and Yang (2004b) and Kim (2004) for discussion of the language’s nominal system.
Most of the discussion here follows Kim and Yang (2005).

10

Then, the question is which part of the grammar makes sure the SUBJ is nom
whereas COMPS is acc. The determination of case value in the VAL is not by alexical process but imposed by syntactic rules. That is, the Head-Subject Rule will
have the following case constraint:(12)

hHead-Subject
i Rule: hi
D E
hd-subj-ph ⇒ 1 CASE | GCASE nom , H[COMPS 1 ]The rule simply says that when a head combines with the SUBJ, the SUBJ element
is nom. As for the case value of a complement, it is a little bit more complicatedsince there are cases where the nonsubject argument gets NOM rather than ACC
as in (8). In the language, nonagentive verbs like coh- ‘be.fond.of’ assign NOM totheir complements. Reflecting this type of case assignment, we adopt the head feature AGT (agentivity) and split the Head-Complement Rule into two as follows:11(13)

a. Head-Complement Rule A:hi

hd-comp-ph

⇒

1

h

i

HEAD | AGT +DECASE | GCASE acc , HCOMPS ..., 1 ,...

b. Head-Complement Rule B:hi

hd-comp-ph

⇒

1

h

i

HEAD | AGT −DECASE | GCASE nom , HCOMPS ..., 1 ,...

Within this system, we then do not need to specify nom to the nonsubject complement of psych verbs, diverging from the traditional literature. Just like otherverbs, the complement(s) of such psych verbs like coh-ta ‘like-DECL’ will bearjust vcase, as a general constraint on verbal elements as represented in (14):
(14)



"



#

POS verb
HEAD

AGT −





 h

i

h




i


ARG-ST NP GCASE vcase , NP GCASE vcase

This lexical information would then project the following partial structure for (8a):11

The positive value of the AGT, similar to STATIVITY, is assigned to the verbs that have an
external argument whereas the negative value is assigned to those with no external argument.

11

(15)

VP






HEAD 1

hd-comp-ph

SUBJ h 2 iooUUUUUUU
UUUU
ooo
o
o
UUUU
o
o
UU
o
o
o
oo
o
V
o
oo

i

GCASE nom.
  ...
 ...
..
 




HEAD 1 [AGT −]

3 NP

h

COMPS h 3 iDARG-STE

2 [GCASE vcase], 3 [GCASE vcase]nokcha-ga

coh-taAs noted here, the verb coh-ta ‘like’ bears the head feature [AGT −]. This meansthat the complement of this verb will get NOM even though in the ARG-ST its
case value is vcase. This is guaranteed by the Head-Complement Rule B in (13).
However, for (8b), the auxiliary verb head ha-ta ‘do’ carries the feature [AGT+] and assigns acc feature to its COMPS element in accordance with the HeadComplement Rule A.
As illustrated here, the KRG can capture simple as well as intriguing case assignment phenomena observed in the language, based on the interaction between
lexical properties and constraints in the grammar rules.
3.3.3

Complex Predicate Constructions

One of the most prevalent constructions in Korean is complex predicates that consist the argument structures of two separate predicates (V2-V1) being brought together somehow or other. Canonical complex predicate constructions include auxiliary and light verb constructions. The constructions are syntactically intriguingin that (a) it is V2 that theta-marks internal arguments and V1 thus has no influence on the number and types of arguments (b) the V2 takes an agentive subject
but inherits its other arguments to the final predicate V1, and (c) V1 and V2 form atight syntactic unit. For example, the two verbs must occur in a fixed order, always
following immediately after a main verb as illustrated in (16b):(16) a.

b.

John-un sakwa-lul mek-ko/*e siph-ess-ta.
John-TOP apple-ACC eat-COMP like-PAST-DECL‘John wanted to eat apples.’*sakwa-lul mek-ko John-un siph-ess-ta.

The syntactic passive construction also forms a complex predicate as a type of
auxiliary construction. As seen from the contrast in the following, syntactic passivewith the auxiliary verb ci-ta has a tight connection with its active form:12(17) a.

haksayngtul-i paci-lul ccic-ess-ta
students-NOM pants-ACC tear-PAST-DECL‘Students tore trousers.’b.

paci-ka ccic-e
ci-ess-ta
pants tear-COMP become-PAST-DECL‘The trousers were torn off.’

The various properties of the construction support the analysis that treats theauxiliary verb and the preceding main verb as a complex predicate, as sketched in
the following structure:
(18)

V



hd-lex-exHEAD 3LEX +



COMPSO h 4 NPi
ww OOOOOH
O
Lexical Arg. ww

ww
ww
w
w
ww

1V

"

LEX +
COMPS h 4 NPi

OOO
O
V





HEAD 3#

AUX +
COMPS h 1 [LEX +]i
ARG-ST h 2 , 1 i

ccic-eci-ess-ta

The structure basically allows the auxiliary verb ci-ess-ta to combine with its lexical complement, the main verb ccic-e ‘tear’. The resulting expression forms a hdlex-ex (head-lexical-expression), first and then inherit the main verb’s arguments to
the resulting expression.12 The combination of these two is licensed by the following grammar rule in the KRG:
(19)

"Head-LEX Rule: #"# "#hd-lex-ex
LEX +
AUX +
→ 1
,H
COMPS A LEX +COMPS A
COMPS h 1 i

The rule specifies that the auxiliary verb ([AUX +]) combines not with a phrasal
but with a lexical (LEX) complement ( 1 ), the result of whose combination is stilla LEX expression. This system, interacting with appropriate lexical entries for
auxiliary verbs, will license a complex-predicate structure and allow us to capture
the syntactic independence of the main verb from the passive auxiliary as well as
the tight syntactic unit between the two.
12

This kind of argument composition is different from previous analyses, mainly in that the composition happens in syntax rather than in the lexicon.

13

The analysis sketched here once again relies on the tight interaction between
lexical information and the grammar rule based on argument composition can successfully parse a variety of complex predicate constructions including auxiliary
constructions, light verb constructions, bound noun constructions, and so forth.13
In particular, we assume that this type of Head-LEX Rule and argument composition is language particular.
3.3.4

Relative Clause Constructions

Unlike English, Korean employs no relative pronouns like who or which. In addition, the predicate of the relative clause preceding the head noun is marked with amorphological marker depending on the type of tense information.14
(20)

Tom-i
chaykii ilk-nun/un/ul
Tom-NOM
read-PRES . PNE / PST. PNE / FUT. PNE book‘the book that Tom reads/read/will read’

The prenominal markers in (20) in a sense function both as a relative pronoun andtense marker. As also expected, the language also allows relativization from anembedded clause:(21)

John-i[Mary-ka
i mekessta-ko] malha-n sakwai
John-NOM Mary-NOM
ate-COMP
say-PNE apple‘the apple that John said Mary ate yesterday’

The key point of our treatment of relative clauses includes the lexical constraints
on the v-rel-mod verb heading the relative clause, a gap-introducing rule, and agrammar rule licensing the combination of a nominal head with a relative clause
modifying it. The lexical constraints on the v-rel-mod will add the feature MOD,guaranteeing that a v-rel-mod element marked with a prenominal ending will modify a nominal element through the head feature MOD. The gap-introducing ruleensures the relative clause to be an incomplete sentence with one missing gap. As
specified in the following feature description in the LKB (Linguistic Knowledge
Building) System, the rule allows any of the elements in the SUBJ or COMPS to
be introduced as a GAP element:15
binary-start-gap-rule-1 := binary-sg &[ SYN.VAL [ SUBJ <>,COMPS <>,
13

The properties of complex predicates can be found in many phenomena in the language. For
the analyses in the similar vein, see Kim and Yang (2004a) for Korean auxiliary constructions, Kim
and Yang (2008) for passive constructions, Kim and Yang (2007) for bound constructions, Kim et al.
(2007) for light verb constructions.
14
Korean also employs the so-called IHRC (internally headed relative clause). The KRG also deals
with such relative clause constructions. See Kim (2006b) for the analysis and its implementation in
the LKB.
15
The symbol ! represents a difference list. See Copestake 2002.

14

GAP <! #2 !> ],ARGS < #1 & [ SYN [ HEAD [ CASE.GCASE nom, PRD - ],VAL [ SUBJ <>, COMPS <> ] ] ],[ SYN.VAL [ SUBJ < #1 >,COMPS < #2 > ] ] > ].

This GAP value is passed upto the tree until it meets its filler to generate a long
distance dependency like (21). For example, the word mek-ess-ta-ko ‘eat-PSTDECL - COMP’ selects two arguments. However, its COMPS can be realized as a
GAP element according to the gap introducing rule described in the above. The
v-rel-mod word malha-n has the information that it modifies a nominal element. In
addition, the relative-clause modifying rule given in the below will terminate thisGAP value when the index value of the GAP is identical with the modified nominal
element:
head-rel-mod-rule := binary &[ SYN.VAL.GAP <! !>ARGS < ph-ex & [ SYN.VAL [ MOD < #1 & [ SYN.HEAD.POS noun,
SEM.INDEX #2 ] >,GAP <! [ SEM.INDEX #2 ] !> ] ],syn-st & #1 & [ SYN.VAL [ GAP <! !>,... ] ] > ].

As indicated in the first element of the ARGS value, the relative clause modifies anominal element whose index value is identical with that of the GAP’s value.
Equipped with these three fundamental mechanisms, the grammar allows us toparse the syntactic as well semantic structures of relative clause constructions. For
example, the following parsed structures and MRS for sentences like (21) are what
the grammar obtains within the system:

15

Leaving aside other semantic relations, we can at least observe that the ARG2
value of eat rel, x2, is coindexed with the ARG0 value of apple rel. The grammar
can correctly parse relative clauses as well as generate a proper MRS meaning
representation.

3.4

Other Phenomena Covered in the Grammar

Including the phenomena we discussed in the previous sections, the first phase of
the KRG also covers major constructions in the language as given in the following
and the results of the implementation are presented in the cited references:•

Multiple Nominative Constructions: Kim et al. (2007)•

Honorification: Kim et al. (2006)•

Coordination: Kim and Yang (2006)•

External and Internally Head Relative Clause: Kim (2006b)•

Bound noun construction: Kim and Yang (2007)•

Comparative construction: Kim et al. (2010)•

Passive construction: Kim and Yang (2008)

Most of these constructions have also been challenges to theoretical linguists. The
first phrase of the KRG has shown us that it is possible to build a deep linguistic
processing grammar for Korean that can be applicable for computational purposes.

3.5

Shortcomings and Issues in the First PhaseAs briefly shown, the first phase of the KRG had been quite successful in termsof analyzing major Korean constructions. Focusing on linguistic data, this phaseof the KRG, however, ran into several important issues concerning efficiency inparsing and lack of a generation system.
That is, the first phase of the KRG, focusing on linguistically significant data,
eventually had limits to parsing a large scale of naturally occurring data, which isa prerequisite to the practical uses of the developed grammar in the area of MT(machine translation). In addition, the first phase KRG focused only on parsing
with no generation module and thus could not be applicable for a real-life use suchas an MT system. The MT architecture the DELPH-IN project employs requires
parsing, transfer, and generation, as shown in Figure 3 (cf. Bond et al. 2005, Bondet al. 2011):
For the KRG to be part of this multilingual-text based MT system, the grammar
needs to be tailored for the other grammars participating in the DELPH-IN consortium so that the KRG’s semantic representations can be compatible with those ofother resource grammars like the ERG and JACY.
16

Figure 3: HPSG/MRS-based MT Architecture
In addition, some types and rules defined in the KRG have caused problems
for generation. In the KRG, the types and rules were defined from linguistic perspectives, not considering generation issues, causing memory overflow errors evenfor some simple cases. In particular, the complex morphological system in the firstphase blocked the generation of most of the sentences headed by a heavily inflectedverb.
Another issue is the refinement of the grammar, in particular with respect to theissue of robust parsing and generation. Consider the following set of examples:16(22) a.

ney/ni cham ippu/yeppu-ney
you/you really pretty-DECL‘You are really pretty.’b.

ney/ni cham ippu/yeppu-kwunc.

ney/ni cham ippu/yeppu-kwunad.

ney/ni cham ippu/yeppu-e

All these examples have variant forms of the subject pronoun, verb form, headverb, and mood suffixes, but have the identical truth conditional meaning. For
example, the second person subject can alternate between ney and ni dependingon the colloquial style. The predicate form can also be either the standard one
yeppu or a colloquial one ippu. The mood suffix can be different too. However, allthese variants need to have the identical MRS representation. Similar phenomena
are prevalent in the language, but the KRG at the first phase is not sophisticatedenough to distinguish such stylistically different sentences.

4

Korean Resource Grammar: the Second Phase

4.1

Directions for the Improvement

One of the main motivations for the grammar improvement in the second phasehas been thus to achieve more balanced approaches between linguistic-based ordeep processing and practical purposes.17 To figure out the problems and issuesof the first phase KRG, we first evaluated its coverage and performance using alarge amount of data. This experiment would enable us to track down what causes
parsing inefficiencies and generating clog.
16
17

As a reviewer point outs, each of these may have different semantic or pragmatic effects.
Part of the discussion here follows Song et al. (2010).

17

In developing the KRG further and to resolve the shortcomings in the firstphase, we have employed two strategies for improvement; (i) using shared grammar libraries and (ii) exploiting large text corpora (using the Korean portions ofmultilingual texts). These two strategies are essential in expanding the coveragewith deep linguistic processing. We shared grammar libraries with the Grammar
Matrix in the grammar Bender et al. (2010) as the foundation of the KRG in thesecond phase. We tried to customize the KRG to the Grammar Matrix more, sothat the KRG can also be part of the multilingual grammar engineering. In addition, we exploit naturally occurring texts as the generalization corpus. To performthe adopted strategies in a more systematic way, we set up our working principles
as following:•

The Grammar Matrix is applied when a judgment about structure (e.g. semantic
representation) is needed.

•

The KRG is applied when a judgment about Korean is needed.

•

The resulting grammar has to run on both PET and LKB without any problems.

•Parsing needs to be accomplished as robustly as possible, and generation needs tobe done as strictly as possible.

4.2

Adding the Generation Module

As pointed out, the important missing part in the first phase of the KRG is a generation module. It was not an easy task to alter the structure of the KRG in thefirst phase from top to bottom in a relatively short time, mainly because the difficulties arise from converting each grammar module (optimized only for parsing)
into something applicable to generation, and further from making the grammar run
separately for parsing and generation.
Accordingly, we first rebuilt the basic schema of the KRG on the Grammar
Matrix customization system, and then imported each grammar module from the
KRG to the matrix-based frame. In addition, we reformed the inflectional hierarchy
to avoid any impediment to generation any longer (S 4.2). We also introduced the
STYLE feature structure to discriminate different sentence (mood) styles. In what
follows, we will see what kind of modifications and improvements we made in thegrammar.

4.3

Modifying the Modular Structures

To improve the system’s compatibility with other resource grammars in the LOGON, we first reclassified and cleaned up the organization of the file systems. Thiswork, though not affecting the grammar’s parsing efficiency, can help the DELPHIN research group refer to the KRG more easily.
Additionally, we revised grammar modules in order to use the Grammar Matrix
to a full extent. In this process, when inconsistencies arise from the previous KRG,18

we followed the strategies and working principles given in 4.1. We further transplanted each previous module into the second phase KRG (KRG2), while checking
the attested test items used in the first phase KRG (KRG1). The test items consistof 6,180 grammatical sentences and 118 ungrammatical sentences, reflecting main
linguistic phenomena in the language.

4.4

Refining the Grammar

4.4.1

Simplifying the Inflectional Hierarchy

As we have noted, the rich inflection system and the complex morphological system built in the KRG1 made generation inefficient. As a way of solving this, we
simplified the inflectional system. As discussed in Kim and Yang (2004b), Korean
has rigid ordering restrictions in the morphological paradigm for verbs, as shownin the following template:(23)

V-base + (Passive/Caussative) + (Hon) + (Tense) + Mood + (Comp)The first phase KRG dealt with this ordering of suffixes by using a type hierarchy
that represents a chain of inflectional slots (Figure 4: Kim and Yang (2004b)).

Figure 4: Korean Verbal Hierarchy

This hierarchy has its own merits, building a verbal element by step-by-stepprocesses from the verb lexeme, as exemplified by an example like the following:(24)

a. [[[[[cap + hi]+si] +ess]+ ta] + ko] ‘catch-Caus-Hon-Past-Decl-Comp’b. v-lxm → v-hon (v-hon-stem) → v-tns (v-tns-stem) → v-free (v-stem) → vcomp

However, this complex system requires a large number of calculations in the generation process. Figure 5 and Table 1 explains the difference in computational
complexity according to each structure.
In Figure 5, (a) is similar to Figure 4, while (b) is on the traditional template approach. Let us compare each complexity to get the target node D. For convenience’sake, let us assume that each node has ten constraints to be satisfied. In (a), since
there are three parents nodes (i.e. A, B, and C) on top of D, D cannot be generated until A, B, and C are checked previously. Hence, it costs at least 10,000(10[A] ×10[B] ×10[C] ×10[D]) calculations. In contrast, in (b), only 100 (10[A]19Figure 5: Calculating Complexity
×10[D]) calculations is enough to generate node D. This means that the deeper thehierarchy is, the more the complexity increases. Table 1 shows (a) requires morethan 52 times as much complexity as (b), though they have the same number ofnodes.
Table 1: Complexity of (a) and (b)
B’
C’
D’
D
Σ

(a)
10[A]×10[B’]
10[A]×10[B]×10[C’]
10[A]×10[B]×10[C]×10[D’]
10[A]×10[B]×10[C]×10[D]

100
1,000
10,00010,00021,100(b)
10[A]×10[B’]
10[A]×10[C’]
10[A]×10[D’]
10[A]×10[D]400

When generation is processed by LKB, all potential inflectional nodes are madebefore syntactic configurations according to the given MRS. Thus, if the hierarchy becomes deeper and contains more nodes, complexity of (a)-styled hierarchy
grows almost by geometric progression. This makes generation virtually impossible, causing memory overflow errors to the generation within the KRG1. A fully
flat structure (b) is not always superior to (a). First of all, the flat approach ignoresthe fact that Korean is an agglutinative language. Korean morphological paradigm
can yield a wide variety of forms; therefore, to enumerate all potential forms is not
only undesirable but also even impossible.
The KRG2 thus follows a hybrid approach (c) that takes each advantage of (a)and (b). (c) is more flattened than (a), which lessens computational complexity. On
the other hand, in (c), the depth of the inflectional hierarchy is fixed as two, andthe skeleton looks like a unary form, though each major node (marked as a bigger
circle) has its own subtypes (marked as dotted lines). Even though the depth has
been diminished, the hierarchy is not a perfectly flat structure; therefore, it canpartially represent the austere suffix ordering in Korean. The hierarchy (c), hereby,
curtails the cost of generation.
4.4.2

Encoding the Sentence Style Information

In the newly developed grammar KRG2, we also introduced the feature STYLE, inorder to enhance the performance of generation. As noted earlier in (22), different
style (largely formal and informal) markings on the mood slot can be used with theidentical truth conditional meaning (or same MRS representations). The choice
between formal or informal sentence styles depends on context:
20

Figure 6: Type Hierarchy of STYLE
A robust parser should cover both styles, but in generation, the system needs tobe consistent. In such a case, the grammar resorts to STYLE to filter out infelicitous
results. The type hierarchy is sketched out in Figure 6. On the other hand, somevariant forms that stem from the corresponding canonical forms falls under robustin Figure 6. For instance, if the text domain for generation is newspaper, we canselect only written as our sentence choice, which excludes other styled sentencesfrom our result.
Let us see (22) again. ni ‘you’ in (22b) is a dialect form of ney, but it has beenused more productively than its canonical form in daily speech. In that case, we
can specify STYLE of ni as dialect as given below. In contrast, the neutral form ney
has an unspecified STYLE feature:ni := n-pn-2nd-non-pl &[ STEM < ‘‘ni’’ >, STYLE dialect ].
ney := n-pn-2nd-non-pl &[ STEM < ‘‘ney’’ > ].

Likewise, since the predicate in (22) ippu ‘pretty’ stems from yeppu in (22), theyshare the predicate name ‘ yeppu a 1 rel’ (i.e. the RMRS standard for predicate
names such as ‘ lemma pos sense rel’), but differ in each STYLE feature. Thismeans the examples in (22) share the same MRS structure regardless of their different style mood markings. These kinds of stylistic differences can take place atthe level of (i) lexicon, (ii) morphological combination, and (iii) syntactic configuration. The KRG2 revised each rule with reference to its style type, yielding total
96 robust rules. As a welcoming result, we could enrich the possible outputs of ourgeneration. Let us call the version reconstructed so far ‘base’.

5

Applications and Evaluation

5.1

Resources

To test the improvements in the grammar, we have used two multilingual corpora:
Sejong Bilingual Corpora: SBC Kim and Cho 2001 and the Basic Travel Expression Corpus: BTEC Kikui et al. 2003. We exploited the Korean parts in each
corpus, taking them as our generalization corpus data. Table 2 represents the configuration of two resources (KoEn: Korean-English, KoJa: Korean-Japanese):21

Table 2: Generalization Corpora
Type
DomainWords
T/T ratio
Avr length

SBC
Bilingual
Balanced Corpus
KoEn : 243,788
KoJa : 276.152
KoEn : 27.63
KoJa : 20.28
KoEn : 16.30
KoJa : 23.30

BTEC
Multilingual
Tourism
914,199
92.7
8.46

We also adopted nine test suites sorted by three types (each test suite includes
500 sentences). As the first type, we used three test sets covering overall sentence structures in Korean; Korean Phrase Structure Grammar (kpsg; Kim (2004)),
Information-based Korean Grammar (ibkg; Chang (1995)), and the SERI test set(seri; Sung and Jang (1997)).
Second, we randomly extracted sentences from each corpus, separately fromour generalization corpus; two suites were taken from the Korean-English andKorean-Japanese pair in SBC (sj-ke and sj-kj, respectively). The other two suites
are from the BTEC-KTEXT (b-k), and the BTEC-CSTAR (b-c); the former consists of relatively plain sentences, while the latter is composed of spoken ones.
Third, we obtained two test suites from sample sentences in two dictionaries;
Korean-English (dic-ke), and Korean-Japanese (dic-kj). These suites assume tohave at least two advantages with respect to our evaluation; (i) the sentence length
is longer than that of BTEC as well as shorter than that of SBC, (ii) the sample sentences on dictionaries are normally made up of useful expressions for translation.

5.2

Methods

We have tried to do experiments and improve the KRG, following the three steps
repeatedly: (i) evaluating, (ii) identifying, and (iii) exploiting. In each step, we first
tried to parse the nine test suites and generate sentences with the MRS structures
obtained from the parsing results, and measured their coverage and performance.
Here, ‘coverage’ means how many sentences can be parsed or generated, and ‘performance’ represents how many seconds it takes on average. In the second step,
we identified the most serious problems. In the third step, we sought to exploit ourgeneralization corpora in order to remedy the drawbacks. After that, we repeatedthe procedures until we obtain the desired results.

5.3

Experiments

The methods just sketched yielded two versions: KRG1 and base. Our further
experiments consist of four phases; lex, MRS, irules, and KRG2.
Expanding the lexicon: To begin with, in order to broaden our coverage, we
expanded our lexical entries with reference to our generalization corpus and previous literature. Verbal items are taken from Song (2007) and Song and Choe

22

(2008), which classify argument structures of Korean verbal lexicon into subtypes
within the HPSG framework in a semi-automatic way.18 For other word classes,
we extracted lexical items from the POS tagged SBC and BTEC corpora. Table 3
explains how many items we extracted from our generalization corpus. Let us callthis version ‘lex’.
Table 3: Expansion of Lexical Items
verbal nouns
verbs and adjectives
common nouns
proper nouns
adverbs
numeral words

4,474
1,216
11,752
7,799
1,757
1,172

MRS: Generation in LKB, as shown in Figure 3, deploys MRS as the input,
which means our generation performance hinges on the well-formedness of MRS.
In other words, if our MRS is broken somewhere or constructed inefficiently, generation outputs are directly affected. For instance, if the semantic representation
does not scope, we will not generate correctly. We were able to identify such sentences by parsing the corpora, storing the semantic representations and then using
the semantic well formedness checkers in the LKB. We identified all rules and lexical items that produced ill-formed MRSs using a small script and fixed them byhand. This had an immediate and positive effect on coverage as well as performance in generation. We refer to these changes as ‘MRS’.
Different inflectional forms for sentence styles: Texts in our daily life are
actually composed of various styles. For example, spoken forms are normallymore or less different from written ones. The difference between them in Korean
is so big that the current version of KRG can hardly parse spoken forms. Besides,
Korean has lots of compound nouns and derived words. Therefore, we included
these forms into our inflectional rules and expanded lexical entries again (3,860
compound nouns, 2,791 derived words). This greatly increased parsing coverage.
We call this version ‘irules’.
Grammaticalized and Lexicalized Forms: There are still remaining problems, because our test suites contain some considerable forms. First, Korean hasquite a few grammaticalized forms; for instance, kupwun is composed of a definite
determiner ku and a classifier for human pwun “the person”, but it functions like asingle word (i.e. a third singular personal pronoun). In a similar vein, there are nota few lexicalized forms as well; for example, a verbal lexeme kkamek- is composedof kka- “peel” and mek- “eat”, but it conveys a sense of “forget”, rather than “peeland eat”. In addition, we also need to cover idiomatic expressions (e.g. “thanks”)
18

We cannot automatically acquire the subcategorization frames for new lexical items from thegiven corpora because of accuracy issues.

23

for robust parsing. Exploiting our corpus, we added 1,720 grammaticalized or lexicalized forms and 352 idioms. Now, we call this ‘KRG2’.
Table 4 compares KRG2 with KRG1, and Figure 7 shows how many lexical
items we have covered so far.
Table 4: Comparison between KRG1 and KRG2
# of default types# of lexical types# of phrasal types# of inflectional rules# of syntactic rules# of lexicon

KRG1
121
289
58
86
36
2,297

KRG2
159
593
106
244
96
39,190Figure 7: Size of the Lexicon

5.4

Evaluating the Results

Table 5 shows the evaluation measure of this study. ‘p’ and ‘g’ stand for ‘parsing’and ‘generation’, respectively. ‘+’ represents the difference compared to KRG1.
Since KRG1 does not generate, there is no ‘g+’.
Table 5: Evaluation of the KRG2
kpsg
ibkg
seri
b-k
b-c
sj-ke
sj-kj
dic-kedic-kj
avr

p
77.0
61.2
71.3
43.0
52.2
35.4
23.0
40.4
34.8
48.7

coverage (%)
p+
g
-5.5
55.2
41.8
68.3
-0.8
65.7
32.6
62.8
45.8
59.4
31.2
58.2
19.6
52.2
31.0
42.6
25.2
67.8
24.5
59.1

s
42.5
41.8
46.8
27.0
31.0
20.6
12.0
17.2
23.6
28.8

ambiguity
p
g
174.9
144.4
990.5
303.5
289.1
128.4
1769.490.0
1175.8
160.6
358.3
170.3
585.9
294.9
1392.7
215.9
789.3
277.9
836.2
198.4

On average, the parsing coverage increases 24.5%. The reason why there arenegative values in ‘p+’ of kpsg and seri is that we discarded some modules thatrun counter efficient processing (e.g., the grammar module for handling floating
quantifiers sometimes produces too many ambiguities.). Since KRG1 has been
constructed largely around the test sets, we expected it to perform well here. If wemeasure the parsing coverage again, after excluding the results of kpsg and seri,

24

90

%

80

kpsg

70

ibkg

60

seri

50

b-k

40

b-c

30

sj-ke

20

sj-kj

10

dic-ke0
KRG1 baselex

MRS irules KRG2

dic-kjFigure 8: Parsing Coverage (%)
90

%

80

kpsg

70

ibkg

60

seri

50

b-k

40

b-c

30

sj-ke

20

sj-kj

10

dic-ke

0
base

lex

MRS

irulesKRG2

dic-kjFigure 9: Generation Coverage (%)
it accounts for 32.5%.19 The generation coverage of KRG2 accounts for almost
60% per parsed sentence on average. Note that KRG1 could not generate at all.
‘s’ (short for ‘success’) means the portion of both parsed and generated sentences(i.e. ‘p’×‘g’), which accounts for about 29%. Ambiguity means ‘# of parses/#of sentences’ for parsing and ‘# of realizations/# of MRSes’ for generation. The
numbers look rather big, which should be narrowed down in our future study.
In addition, we can find out in Table 5 that there is a coverage ordering withrespect to the type of test suites; ‘test sets > BTEC > dic > SBC’. It is influenced
by three factors; (i) lexical variety, (ii) sentence length, and (iii) text domain. Thisdifference implies that it is highly necessary to use variegated texts in order toimprove grammar in a comprehensive way.
Figure 8 to 11 represent how much each experiment in Section 5.3 contributes
to improvement. First, let us see Figure 8 and 9. As we anticipated, lex and irules
contribute greatly to the growth of parsing coverage. In particular, the line of b-c in
Figure 9, which mostly consists of spoken forms, rises rapidly in irules and KRG2.
That implies Korean parsing largely depends on richness of lexical rules. On theother hand, as we also expected, MRS makes a great contribution to generation
19
The running times, meanwhile, becomes slower as we would expect for a grammar with greater
coverage. However, we can make up for it using the PET parser, as shown in Figure 10.

25

25.0
kpsg

sec.

20.0

ibkg
seri

15.0

b-k
b-c

10.0

sj-ke
sj-kj

5.0

dic-ke0.0
KRG1 baselex

MRS irules KRG2

dic-kjFigure 10: Parsing Performance (s)
25.0
kpsg

sec.

20.0

ibkg
seri

15.0

b-k
b-c

10.0

sj-ke
sj-kj

5.0

dic-ke
0.0
base

lex

MRS

irules KRG2

dic-kjFigure 11: Generation Performance (s)coverage (Figure 9). In MRS, the growth accounts for 22% on average. That
implies testing with large corpora must take precedence in order for coverage to
grow.
Figure 10 and 11 shows performance in parsing and generation, respectively.
Comparing to KRG1, our Matrix-based grammars (from base to KRG2) yields
fairly good performance. It is mainly because we deployed the PET parser thatruns fast, whereas KRG1 runs only on LKB. Figure 11, on the other hand, showsthat the revision of MRS also does much to enhance generation performance, in
common with coverage mentioned before. It decreases the running times by about
3.1 seconds on average.

5.5

Future Directions

The second phase of the grammar, KRG2, has improved the efficiency of the deep
processing grammar significantly. In particular, the addition of the generation module opened a new direction for the KRG such as building deep-processed rich treebanks and developing an MT system. However, for the grammar to cover a wider
ranger of naturally occurring data, whose results can be ultimately applied to NLP
applications, much more work needs to be done as briefly summarized in the following:26

•refining the current KRG so that it can efficiently cover phenomena such as pro drop,
serial verb construction, coordination and subjunction, ellipsis, wh-constructions,other semantics-related phenomena, etc.

•

developing fine-grained semantics using MRS that can capture scope, event structures, message types, linking between syntax and semantics•

incrementally increasing coverage of clause internal syntax in Korean•

incorporating the use of default entries for words unknown to the Korean lexicon•

testing with authentic corpora and expanding more coverage•developing a more sophisticated generator that can be used for real-life applications•

developing an MT system based on deep processing grammars

6

Conclusion

The second phase of the KRG has been successfully included in the LOGON repository and it is now available online (http://krg.khu.ac.kr). It is hard to deny the factthat in building an efficient grammar, expressive accuracy has often been sacrificed
in order to achieve computational tractability (Siegel 2002, Oepen et al. 2002).
However, putting linguistic generalizations aside has brought difficulties expanding the coverage and eventually building a large scale of grammar. The morphological, syntactic, and semantic parsing system we have developed here with typed
feature structures is an effort to solve such preexisting problems while keeping linguistic insights, thus making the Korean morphology, syntax, and semantics much
simpler.
The work described here, even though it is an on-going project, achieves promising coverage of major constructions that have been widely discussed in the literature as well as on real-life data. The research presented in this paper provides robust, deep parsing results and introduces effective generation output too. We hopeto have shown that the ongoing project of developing the KRG is prospective in
two respects: as a means of testing linguistic hypotheses and seeking the potential
to be integrated in applications which require natural language understanding, including machine translation, question-answering system, text summarization, andso forth.

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