Semantic Analysis of Japanese Noun Phrases : 
A New Approach to Dictionary-Based Understanding 
Sadao Kurohashi and Yasuyuki Sakai 
Graduate School of Informatics, Kyoto University 
Yoshida-honmachi, Sakyo, Kyoto, 606-8501, Japan 
kuro0i, kyoto-u, ac. jp 
Abstract 
This paper presents a new method of analyz- 
ing Japanese noun phrases of the form N1 no 
5/2. The Japanese postposition no roughly cor- 
responds to of, but it has much broader us- 
age. The method exploits a definition of N2 
in a dictionary. For example, rugby no coach 
can be interpreted as a person who teaches tech- 
nique in rugby. We illustrate the effectiveness 
of the method by the analysis of 300 test noun 
phrases. 
1 Introduction 
The semantic analysis of Japanese noun phrases 
of the form N1 no N2 is one of the difficult prob- 
lems which cannot be solved by the current ef- 
forts of many researchers. Roughly speaking, 
Japanese noun phrase N1 no N2 corresponds to 
English noun phrase N2 of N1. However, the 
Japanese postposition no has much broader us- 
age than of as follows: 
watashi 'I' no kuruma 'car' 
tsukue 'desk' no ashi 'leg' 
gray no seihuku 'uniform' 
possession 
whole-part 
modification 
senmonka 'expert' no chousa 'study' 
agent 
rugby no coach subject 
yakyu 'baseball' no senshu 'player' category 
kaze 'cold' no virus result 
ryokou 'travel' no jyunbi 'preparation' purpose 
toranpu 'card' no tejina 'trick' instrument 
The conventional approach to this problem 
was to classify semantic relations, such as pos- 
session, whole-part, modification, and others. 
Then, classification rules were crafted by hand, 
or detected from relation-tagged examples by 
a machine learning technique (Shimazu et al., 
1987; Sumita et al., 1990; Tomiura et al., 1995; 
Kurohashi et al., 1998). 
The problem in such an approach is to set 
up the semantic relations. For example, the 
above examples and their classification came 
from the IPA nominal dictionary (Information- 
Technology Promotion Agency, Japan, 1996). 
Is it possible to find clear boundaries among 
subject, category, result, purpose, instrument, 
and others? No matter how fine-grained rela- 
tions we set up, we always encounter phrases 
which are on the boundary or belong to two or 
more relations. 
This paper proposes a completely different 
approach to the task, which exploits semantic 
role information of nouns in an ordinary dictio- 
nary. 
2 Semantic Roles of Nouns 
The meaning of a word can be recognized by 
the relationship with its semantic roles. In the 
case of verbs, the arguments of the predicates 
constitute the semantic roles, and a consider- 
able number of studies have been made. For 
example, the case grammar theory is a semantic 
valence theory that describes the logical form of 
a sentence in terms of a predicate and a series 
of case-labeled arguments such as agent, object, 
location, source, goal (Fillmore, 1968). Further- 
more, a wide-coverage dictionary describing se- 
mantic roles of verbs in machine readable form 
has been constructed by a great deal of labor 
(Ikehara et al., 1997). 
Not only verbs, but also nouns can have se- 
mantic roles. For example, coach is a coach of 
some sport; virus is a virus causing some dis- 
ease. Unlike the case of verbs, no semantic- 
481 
Table 1: Semantic relations in N1 no N2 
Relation Noun Phrase N1 no N2 Verb Phrase 
Semantic-role rugby no coach, 
kaze 'cold' no virus, 
tsukue 'desk' no ashi 'leg', 
ryokou 'travel' no jyunbi 'preparation' 
hon-wo 'book-Ace' yomu 'read' 
Agent senmonka 'expert' no chousa 'study' kare-ga 'he-NOM' yomu 'read' 
Possession watashi 'I' no kuruma 'car' 
Belonging gakkou 'school' no sensei 'teacher' 
Time aki 'autumn' no hatake 'field' 3ji-ni 'at 3 o'clock' yomu 'read' 
Place Kyoto no raise 'store' heya-de 'in room' yomu 'read' 
Modification gray no seihuku 'uniform' isoide 'hurriedly' yomu 'read' 
huzoku 'attached' no neji 'screw' 
ki 'wooden' no hako 'box' 
Complement kimono no jyosei 'lady' 
nobel-sho 'Nobel prize' no kisetsu 'season' 
role dictionary for nouns has been constructed 
so far. However, in many cases, semantic roles 
of nouns are described in an ordinary dictio- 
nary for human being. For example, a Japanese 
dictionary for children, Reikai Shougaku Koku- 
gojiten (abbreviated to RSK) (Tadil~, 1997), 
gives the definition of the word coach and virus 
as follows 1: 
coach a person who teaches technique in some 
sport 
virus a living thing even smaller than bacte- 
ria which causes infectious disease like in- 
fluenza 
If an NLP system can utilize these definitions 
as they are, we do not need to take the trou- 
ble in constructing a semantic-role dictionary 
for nouns in the special format for machine-use. 
3 Interpretation of N1 no N2 using a 
Dictionary 
Semantic-role information of nouns in an ordi- 
nary dictionary can be utilized to solve the dif- 
ficult problem in the semantic analysis of N1 
1Although our method handles Japanese noun 
phrases by using Japanese definition sentences, in this 
paper we use their English translations for the explana- 
tion. In some sense, the essential point of our method is 
language-independent. 
no N2 phrases. In other words, we can say the 
problem disappears. 
For example, rugby no coach can be inter- 
preted by the definition of coach as follows: the 
dictionary describes that the noun coach has an 
semantic role of sport, and the phrase rugby no 
coach specifies that the sport is rugby. That is, 
the interpretation of the phrase can be regarded 
as matching rugby in the phrase to some sport 
in the coach definition. Furthermore, based on 
this interpretation, we can paraphrase rugby no 
coach into a person who teaches technique in 
rugby, by replacing some sport in the definition 
with rugby. 
Kaze 'cold' no virus is also easily interpreted 
based on the definition of virus, linking kaze 
'cold' to infectious disease. 
Such a dictionary-based method can handle 
interpretation of most phrases where conven- 
tional classification-based analysis failed. As a 
result, we can arrange the diversity of N1 no N2 
senses simply as in Table 1. 
The semantic-role relation is a relation that 
N1 fills in an semantic role of N2. When N2 is 
an action noun, an object-action relation is also 
regarded as a semantic-role relation. 
On the other hand, in the agent, posses- 
sion and belonging relations, N1 and N2 have 
a weaker relationship. In theory, any action can 
be done by anyone (my study, his reading, etc.); 
482 
anything can be possessed by anyone (my pen, 
his feeling, etc.); and anyone can belong to any 
organization (I belong to a university, he be- 
longs to any community, etc.). 
The difference between the semantic-role re- 
lation and the agent, possession, belonging rela- 
tions can correspond to the difference between 
the agent and the object of verbs. In general, 
the object has a stronger relationship with a 
verb than the agent, which leads several asym- 
metrical linguistic phenomena. 
The time and place relations have much 
clearer correspondence to optional cases for 
verbs. A modification relation is also parallel 
to modifiers for verbs. If a phrase has a modi- 
fication relation, it can be paraphrased into N2 
is N1, like gray no seihuku 'uniform' is para- 
phrased into seihuku 'uniform' is gray. 
The last relation, the complement relation is 
the most difficult to interpret. The relation be- 
tween N1 and N2 does not come from Nl'S se- 
mantic roles, or it is not so weak as the other 
relations. For example, kimono no jyosei 'lady' 
means a lady wearing a kimono, and nobel-sho 
'Nobel prize' no kisetsu 'season' means a sea- 
son when the Nobel prizes are awarded. Since 
automatic interpretation of the complement re- 
lation is much more difficult than that of other 
relations, it is beyond the scope of this paper. 
4 Analysis Method 
Once we can arrange the diversity of N1 no N 2 
senses as in Table 1, their analysis becomes very 
simple, consisting of the following two modules: 
1. Dictionary-based analysis (abbreviated to 
DBA hereafter) for semantic-role relations. 
2. Semantic feature-based analysis (abbrevi- 
ated to SBA hereafter) for some semantic- 
role relations and all other relations. 
After briefly introducing resources employed, 
we explain the algorithm of the two analyses. 
4.1 Resources 
4.1.1 RSK 
RSK (Reikai Shougaku Kokugojiten), a 
Japanese dictionary for children, is used to find 
semantic roles of nouns in DBA. The reason 
why we use a dictionary for children is that, 
generally speaking, definition sentences of such 
a dictionary are described by basic words, 
which helps the system finding links between 
N1 and a semantic role of a head word. 
All definition sentences in RSK were analyzed 
by JUMAN, a Japanese morphological analyzer, 
and KNP, a Japanese syntactic and case ana- 
lyzer (Kurohashi and Nagao, 1994; Kurohashi 
and Nagao, 1998). Then, a genus word for a 
head word, like a person for coach were detected 
in the definition sentences by simple rules: in a 
Japanese definition sentence, the last word is a 
genus word in almost all cases; if there is a noun 
coordination at the end, all of those nouns are 
regarded as genus words. 
4.1.2 NTT Semantic Feature 
Dictionary 
NTT Communication Science Laboratories 
(NTT CS Lab) constructed a semantic feature 
tree, whose 3,000 nodes are semantic features, 
and a nominal dictionary containing about 
300,000 nouns, each of which is given one or 
more appropriate semantic features. Figure 1 
shows the upper levels of the semantic feature 
tree. 
SBA uses the dictionary to specify conditions 
of rules. DBA also uses the dictionary to cal- 
culate the similarity between two words. Sup- 
pose the word X and Y have a semantic feature 
Sx and Sy, respectively, their depth is dx and 
dy in the semantic tree, and the depth of their 
lowest (most specific) common node is de, the 
similarity between X and Y, sire(X, Y), is cal- 
culated as follows: 
sire(X, Y) = (dc x 2)/(dx + dy). 
If Sx and Sy are the same, the similarity is 1.0, 
the maximum score based on this criteria. 
4.1.3 NTT Verb Case Frame 
Dictionary 
NTT CS Lab also constructed a case frame 
dictionary for 6,000 verbs, using the semantic 
features described above. For example, a case 
frame of the verb kakou-suru (process) is as fol- 
lows: 
N1 (AGENT)-ga N2(CONCRETE)-wo kako.u-suru 
'N1 process N2' 
where ga and wo are Japanese nominative and 
accusative case markers. The frame describes 
483 
NOUN 
CONCRETE 
J 
AGENT PLACE /\ 
HUMAN ORGANIZATION 
CONCRETE 
ABSTRACT 
J 
ABSTRACT EVENT ABSTRACT RELATION 
J/l\  
TIME POSITION QUANTITY .... 
Figure 1: The upper levels of NTT Semantic Feature Dictionary. 
that the verb kakou-suru takes two cases, nouns 
of AGENT semantic feature can fill the ga-case 
slot and nouns of CONCRETE semantic feature 
can fill the wo-case slot. KNP utilizes the case 
frame dictionary for the case analysis. 
4.2 Algorithm 
Given an input phrase N1 no N2, both DBA and 
SBA are applied to the input, and then the two 
analyses are integrated. 
4.2.1 Dictionary-based Analysis 
Dictionary based-Analysis (DBA) tries to find 
a correspondence between N1 and a semantic 
role of N2 by utilizing RSK, by the following 
process: 
1. Look up N2 in RSK and obtain the defini- 
tion sentences of N2. 
2. For each word w in the definition sentences 
other than the genus words, do the follow- 
ing steps: 
2.1. When w is a noun which shows a 
semantic role explicitly, like kotog- 
ara 'thing', monogoto 'matter', nanika 
'something', and N1 does not have a 
semantic feature of HUMAN or TIME, 
give 0.9 to their correspondence 2. 
2.2. When w is other noun, calculate the 
similarity between N1 and w by using 
NTT Semantic Feature Dictionary (as 
described in Section 4.1.2), and give 
2For the present, parameters in the algorithm were 
given empirically, not optimized by a learning method. 
the similarity score to their correspon- 
dence. 
2.3. When w is a verb, it has a vacant case 
slot, and the semantic constraint for 
the slot meets the semantic feature of 
N1, give 0.5 to their correspondence. 
. 
. 
If we could not find a correspondence with 
0.6 or more score by the step 2, look up the 
genus word in the RSK, obtain definition 
sentences of it, and repeat the step 2 again. 
(The looking up of a genus word is done 
only once.) 
Finally, if the best correspondence score is 
0.5 or more, DBA outputs the best corre- 
spondence, which can be a semantic-role 
relation of the input; if not, DBA outputs 
nothing. 
For example, the input rugby no coach is ana- 
lyzed as follows (figures attached to words indi- 
cate the similarity scores; the underlined score 
is the best): 
(1) rugby no coach 
coach a person who teaches technique0.21 
in some sport 1.0 
Rugby, technique and sport have the semantic 
feature SPORT, METHOD and SPORT respectively 
in NTT Semantic Feature Dictionary. The low- 
est common node between SPORT and METHOD 
is ABSTRACT, and based on these semantic fea- 
tures, the similarity between rugby and tech- 
nique is calculated as 0.21. On the other hand, 
484 
the similarity between rugby and sport is calcu- 
lated as 1.0, since they have the same seman- 
tic feature. The case analysis finds that all case 
slots of teach are filled in the definition sentence. 
As a result, DBA outputs the correspondence 
between rugby and sport as a possible semantic- 
role relation of the input. 
On the other hand, bunsho 'writings' no tat- 
sujin 'expert' is an example that N1 corresponds 
to a vacant case slot of the predicate outstand- 
ing: 
(2) bunshou 'writings' no tatsujin 'expert' 
expert a person being outstanding (at 
¢0.50) 
Puroresu 'pro wrestling' no chukei 'relay' is 
an example that the looking up of a genus word 
broadcast leads to the correct analysis: 
(3) puroresu 'pro wrestling' no chukei 'relay' 
relay a relay broadcast 
broadcast a radioo.o or televisiono.o 
presentation of news 0.48, 
entertainment 0.87, music o.so and 
others 
4.2.2 Semantic Feature-based Analysis 
Since diverse relations in N1 no N2 are han- 
dled by DBA, the remaining relations can be 
detected by simple rules checking the semantic 
features of N1 and/or N2. 
The following rules are applied one by one to 
the input phrase. Once the input phrase meets 
a condition, SBA outputs the relation in the 
rule, and the subsequent rules are not applied 
any more. 
1. NI:HUMAN, N2:RELATIVE --~ semantic- 
role(relative) 
e.g. kare 'he' no oba 'aunt' 
2. NI:HUMAN, N2:PERSONAL._RELATION --~ 
semantic-role(personal relation) 
e.g. kare 'he' no tomodachi 'friend' 
3. NI:HUMAN, N2:HUMAN --~ modifica- 
tion(apposition) 
e.g. gakusei 'student' no kare 'he' 
4. NI:ORGANIZATION, N2:HUMAN ~ belong- 
ing 
e.g. gakkou 'school' no sensei 'teacher' 
5. NI:AGENT, N2:EVENT ~ agent 
e.g. senmonka 'expert' no chousa 'study' 
6. NI:MATERIAL, N2:CONCRETE --+ modifica- 
tion(material) 
e.g. ki 'wood' no hako 'box' 
7. NI:TIME, N2:* 3 ___+ time 
e.g. aki 'autumn' no hatake 'field' 
8. NI:COLOR, QUANTITY, or FIGURE, g2:* 
modification 
e.g. gray no seihuku 'uniform' 
9. gl:*, N2:QUANTITY ~ semantic-role(at- 
tribute) 
e.g. hei 'wall' no takasa 'height' 
10. gl:* , N2:POSITION ~ semantic-role(posi- 
tion) 
e.g. tsukue 'desk' no migi 'right' 
11. NI:AGENT, Y2:* ~ possession 
e.g. watashi f no kuruma 'car' 
12. NI:PLACE or POSITION, N2:* ---* place 
e.g. Kyoto no mise 'store' 
The rules 1, 2, 9 and 10 are for certain 
semantic-role relation. We use these rules be- 
cause these relations can be analyzed more ac- 
curately by using explicit semantic features, 
rather than based on a dictionary. 
4.2.3 Integration of Two Analyses 
Usually, either DBA or SBA outputs some rela- 
tion. In rare cases, neither analysis outputs any 
relation, which means analysis failure. When 
both DBA and SBA output some relations, the 
results are integrated as follows (basically, if the 
output of the one analysis is more reliable, the 
output of the other analysis is discarded): 
If a semantic-role relation is detected by SBA, 
discard the output from DBA. 
Else if the correspondence of 0.95 or more 
score is detected by DBA, 
discard the output from SBA. 
Else if some relation is detected by SBA, 
discard the output from DBA if the corre- 
spondence score is 0.8 or less. 
In the case of the following example, rojin 'old 
person' no shozo 'portrait', both analyses were 
accepted by the above criteria. 
3,., meets any noun. 
485 
Table 2: Experimental results of N1 no N2 analysis. 
Relation (R) 
Semantic-role (DBA) 
Semantic-role (SBA) 
Agent 
Possession 
Belonging 
Time 
Place 
Modification 
Correct R is correct, but the R was detected, 
detected correspon- but incorrect 
dence was incorrect 
R was not detected, 
though R is possibly 
correct 
137 19 21 19 
15 -- 2 0 
10 -- 1 2 
32 -- 7 0 
12 -- 1 2 
20 -- 1 0 
23 -- 7 2 
20 -- 3 21 
(4) rojin 'old person' no shozo 'portrait' 
DBA : 
portrait a painting0.17 or photograph0.17 
of a face0.1s or figure0.0 of real 
person 0.s4 
SBA : NI:AGENT , N2:* ----+ possession 
DBA interpreted the phrase as a portrait on 
which an old person was painted; SBA detected 
the possession relation which means an old per- 
son possesses a portrait. One of these interpre- 
tations would be preferred depending on con- 
text, but this is a perfect analysis expected for 
N1 no N2 analysis. 
5 Experiment and Discussion 
5.1 Experimental Evaluation 
We have collected 300 test N1 no N2 phrases 
from EDR dictionary (Japan Electronic Dic- 
tionary Research Institute Ltd., 1995), IPA 
dictionary (Information-Technology Promotion 
Agency, Japan, 1996), and literatures on N1 no 
N2 phrases, paying attention so that they had 
enough diversity in their relations. Then, we 
analyzed the test phrases by our system, and 
checked the analysis results by hand. 
Table 2 shows the reasonably good result 
both of DBA and SBA. The precision of DBA, 
the ratio of correct analyses to detected anal- 
yses, was 77% (=137/(137+19+21)); the re- 
call of DBA, the ratio of correct analyses 
to potential semantic-role relations, was 78% 
(=137/(137+19+19)). The result of SBA is also 
good, excepting modification relation. 
Some phrases were given two or more rela- 
tions. On average, 1.1 relations were given to 
one phrase. The ratio that at least one correct 
relation was detected was 81% (=242/300); the 
ratio that all possibly correct relations were de- 
tected and no incorrect relation was detected 
was 73% (=219/300). 
5.2 Discussion of Correct Analysis 
The success ratio above was reasonably good, 
but we would like to emphasize many interesting 
and promising examples in the analysis results. 
(5) mado 'window' no curtain 'curtain' 
curtain a hanging cloth that can be 
drawn to cover a window1.0 in a 
room0.s3, to divide a room0.s3, etc. 
(6) osetsuma 'living room' no curtain 'curtain' 
curtain a hanging cloth that can be 
drawn to cover a window0.s2 in a 
room 1.0, to divide a room 1.0, etc. 
(7) oya 'parent' no isan 'legacy' 
lagacy property left on the death of 
the owner 0.s4 
Mado 'window' no curtain must embarrass 
conventional classification-based methods; it 
might be place, whole-part, purpose, or some 
other relation like being close. However, DBA 
can clearly explain the relation. Osetuma 'liv- 
ing room' no curtain is another interestingly an- 
alyzed phrase. DBA not only interprets it in a 
simple sense, but also provides us with more in- 
teresting information that a curtain might be 
being used for partition in the living room. 
486 
The analysis result of oya 'parent' no isan 
'legacy' is also interesting. Again, not only the 
correct analysis, but also additional information 
was given by DBA. That is, the analysis result 
tells us that the parent died. Such information 
would facilitate intelligent peformance in a dia- 
logue system analyzing: 
User : I bought a brand-new car by the legacy 
from my parent. 
System : Oh, when did your parent die? I 
didn't know that. 
By examining these analysis results, we 
can conclude that the dictionary-based un- 
derstanding approach can provide us with 
much richer information than the conventional 
classification-based approaches. 
5.3 Discussion of Incorrect Analysis 
It is possible to classify some of the causes of 
incorrect analyses arising from our method. 
One problem is that a definition sentence does 
not always describe well the semantic roles as 
follows: 
(8) shiire 'stocking' no saikaku 'resoucefulness' 
resoucefulness the ability to use one's 
head 0.1s cleverly 
Saikaku 'resourcefulness' can be the ability for 
some task, but the definition says nothing about 
that. On the other hand, the definition of 
sainou 'talent' is clearer about the semantic role 
as shown below. Concequently, shii~e 'stocking' 
no sainou 'tMent' can be interpretted correctly 
by DBA. 
(9) shiire 'stocking' no sainou 'talent' 
talent power and skill, esp. to do 
something 0.90 
This represents an elementary problem of our 
method. Out of 175 phrases which should be 
interpreted as semantic-role relation based on 
the dictionary, 13 were not analyzed correctly 
because of this type of problem. 
However, such a problem can be solved by 
revising the definition sentences, of course in 
natural language. This is a humanly reason- 
able task, very different from the conventional 
approach where the classification should be re- 
considered, or the classification rules should be 
modified. 
Another problem is that sometimes the simi- 
larity calculated by NTT semantic feature dic- 
tionary is not high enough to correspond as fol- 
lows: 
(10) ume 'ume flowers' no meisho 'famous place' 
famous place a place being famous for 
scenery 0.20, etc. 
In some cases the structure of NTT semantic 
feature dictionary is questionable; in some cases 
a definition sentence is too rigid; in other cases 
an input phrase is a bit metaphorical. 
As for SBA, most relations can be detected 
well by simple rules. However, it is not possible 
to detect a modification relation accurately only 
by using NTT semantic feature dictionary, be- 
cause modifier and non-modifier nouns are often 
mixed in the same semantic feature category. 
Other proper resource should be incorporated; 
one possibility is to use the dictionary definition 
of N1. 
6 Related Work 
From the view point of semantic roles of nouns, 
there have been several related research con- 
ducts: the mental space theory is discussing 
the functional behavior of nouns (Fauconnier, 
1985); the generative lexicon theory accounts 
for the problem of creative word senses based 
on the qualia structure of a word (Pustejovsky, 
1995); Dahl et al. (1987) and Macleod et al. 
(1997) discussed the treatment of nominaliza- 
tions. Compared with these studies, the point 
of this paper is that an ordinary dictionary can 
be a useful resource of semantic roles of nouns. 
Our approach using an ordinary dictionary 
is similar to the approach used to creat Mind- 
Net (Richardson et al., 1998). However, the se- 
manitc analysis of noun phrases is a much more 
specialized and suitable application of utilizing 
dictionary entries. 
7 Conclusion 
The paper proposed a method of analyzing 
Japanese N1 no N2 phrases based on a dictio- 
nary, interpreting obscure phrases very clearly. 
The method can be applied to the analysis of 
compound nouns, like baseball player. Roughly 
speaking, the semantic diversity in compound 
nouns is a subset of that in N1 no N2 phrases. 
Furthermore, the method must be applicable to 
487 
the analysis of English noun phrases. The trans- 
lated explanation in the paper naturally indi- 
cates the possibility. 
Acknowledgments 
The research described in this paper was sup- 
ported in part by JSPS-RFTF96P00502 (The 
Japan Society for the Promotion of Science, Re- 
search for the Future Program) and Grant-in- 
Aid for Scientific Research 10143209. 

References 
Deborah A. DaM, Martha S. Palmer, and Re- 
becca J. Passonneau. 1987. Nominalizations 
in PUNDIT. In Proceedings of the 25th An- 
nual Meeting of ACL, pages 131-139, Stan- 
ford, California. 
Gilles Fauconnier. 1985. Mental Spaces : as- 
pects of meaning construction in natural lan- 
guage. The MIT Press. 
Charles J. Fillmore. 1968. The case for case. 
Holt, Rinehart and Winston, New York. 
Satoru Ikehara, Masahiro Miyazaki, Satoshi 
Shirai, Akio Yokoo, Hiromi Nakaiwa, Ken- 
tarou Ogura, and Yoshifumi Oyama Yoshi- 
hiko Hayashi, editors. 1997. Japanese Lexi- 
con. Iwanami Publishing. 
Information-Technology Promotion Agency, 
Japan. 1996. Japanese Nouns : A Guide to 
the IPA Lexicon of Basic Japanese Nouns. 
Japan Electronic Dictionary Research Institute 
Ltd. 1995. EDR Electronic Dictionary Spec- 
ifications Guide. 
Sadao Kurohashi and Makoto Nagao. 1994. A 
syntactic analysis method of long Japanese 
sentences based on the detection of conjunc- 
tive structures. Computational Linguistics, 
20(4). 
Sadao Kurohashi and Makoto Nagao. 1998. 
Building a Japanese parsed corpus while im- 
proving the parsing system. In Proceedings of 
the First International Conference on Lan- 
guage Resources ~ Evaluation, pages 719- 
724. 
Sadao Kurohashi, Masaki Murata, Yasunori 
Yata, Mitsunobu Shimada, and Makoto 
Nagao. 1998. Construction of Japanese 
nominal semantic dictionary using "A NO 
B" phrases in corpora. In Proceedings of 
COLING-A CL '98 workshop on the Computa- 
tional Treatment of Nominals. 
Catherine Macleod, Adam Meyers, Ralph Gr- 
ishman, Leslie Barrett, and Ruth Reeves. 
1997. Designing a dictionary of derived nom- 
inals. In Proceedings of Recent Advances in 
Natural Language Processing, Tzigov Chark, 
Bulgaria. 
James Pustejovsky. 1995. The Generative Lex- 
icon. The MIT Press. 
Stephen D. Richardson, William B. Dolan, and 
Lucy Vanderwende. 1998. Mindnet: ac- 
quiring and structuring semantic informa- 
tion from text. In Proceedings of COLING- 
A CL '98. 
Akira Shimazu, Shozo Naito, and Hirosato No- 
mura. 1987. Semantic structure analysis of 
Japanese noun phrases wirh adnominal parti- 
cles. In Proceedings of the 25th Annual Meet- 
ing of ACL, pages 123-130, Stanford, Califor- 
nia. 
Eiichiro Sumita, Hitoshi Iida, and Hideo Ko- 
hyama. 1990. Translating with examples: A 
new approach to machine translation. In Pro- 
ceedings of the 3rd TMI, pages 203-212. 
Jyunichi Tadika, editor. 1997. Reika Shougaku 
Kokugojiten (Japanese dictionary for chil- 
dren). Sanseido. 
Yoichi Tomiura, Teigo Nakamura, and Toru Hi- 
taka. 1995. Semantic structure of Japanese 
noun phrases NP no NP (in Japanese). 
Transactions of Information Processing Soci- 
ety of Japan, 36(6):1441-1448. 
