SOME LINGUISTIC ASPECTS FOR AUTOMATIC TEXT UNDERSTANDING 
Yutaka Kusanagi 
Institute of Literature and Linguistics 
University of Tsukuba 
Sakura-mura, Ibarakl 305 JAPAN 
ABSTRACT 
This paper proposes a system of map- 
ping classes of syntactic structures as 
instruments for automatic text under- 
standing. The system illustrated in Japa- 
nese consists of a set of verb classes and 
information on mapping them together with 
noun phrases, tense and aspect. The sys- 
tem. having information on direction of 
possible inferences between the verb 
classes with information on tense and as- 
pect, is supposed to be utilized for rea- 
soning in automatic text understanding. 
I. INTRODUCTION 
The purpose of this paper is to pro- 
pose a system of mapping classes of syn- 
tactic structures as instruments for auto- 
matic text understanding. The system con- 
sists of a set of verb classes and Jnfor- 
matlon on mapping them together with noun 
phrases, tense and aspect, and \]s supposed 
to he utilized for inference in automatic 
text understanding. 
The language used for illustration of 
the system is Japanese. 
There Is a tendency for non-syntactic 
analysers and semantic grammars In auto- 
matic text understanding. However. this 
proposal Is motivated by the fact that 
syntactic structures, once analyzed and 
classified in terms of semantic related- 
ness, provide much information for" under- 
standing. This is supported by the fact 
that human beings use syntactically re- 
lated sentences when they ask questions 
about texts. 
The system we are proposing has the 
following elements: 
1) Verb classes. 
2) Mapping of noun phrases between 
or among some verb classes. 
3) Direction of possible infel'ence 
between the classes with information on 
tense and aspect. 
Our experiment, in which subjects are 
asked to make true-false questions about 
certain texts, revealed that native speak- 
ers think that they understand texts by 
deducting sentences lexically or semanti- 
cally related. For instance, a human being 
relates questions such as 'Did Mary go to 
a theater?' to a sentence in texts such as 
'John took Mary to a theater.' Or, by the 
same sentence, he understands that 'Mary 
was in the theater." 
II. FEATURES OF THE JAPANESE SYNTAX 
Features of ,Japanese syntax relevant 
to the discussion in this paper are pre- 
sented below. 
The sentence usually ha:# case mark- 
ings as postpositions to noun phrases. For 
instance. 
I. John qa Mary D_J_ himitsu o hanashita 
'John told a secret to Mary.' 
In sentence 1. postpositions ga. ni and 
o indicate nominative, dative alld accusa- 
tive. respectively. 
409 
However. postposJtions do not unique- 
{y map to deep cases. Take the followitlg 
sentences for example. 
2. John ~ia_ sanii B i_ itta. 
"John went at :? o'cio(-k.' 
3. John w_a Tokyo r!t itta. 
"John ~,~'ellt to Tokyo." 
4. Johr~ w;~ Tokyo ILI :~undeiru. 
'John lives in Tokyo.' 
Ni in the sentences 2, 3. 4 indicate time. 
goal and location, respectively. This is 
due to the verb ca|egory (3 and 41 OF the 
class of noun phrases (2 and 31 appearing 
in each sentence. 
Certain mor'phemc classes hide the 
casemark ing. e.g. 
5. John ~Q itta. 
"John also went (y;omewhere). 
6. Tokyo mo itta. 
'Someone went to Tokyo also.' 
The mo in sentence 5 and 6 means 'also'. 
Therefore these sentences are derived from 
different syntactical constructions, that 
is. sentences 7 and 8. respectively. 
7. John ga itta. 
"John went (somewhere).' 
8. Tokyo n__ki itta. 
• Someone went to Tokyo." 
Furthermore. as illustrated in sen- 
tences 5 through 6, noun phrases ,lay be 
deleted freely, provided the context 
gives full information. In sentences 6 and 
7. a noun phrase indicating the goal is 
missing and sentences 6 and 8 lack thal 
indicating the subject. Finally. there 
are many pairs of lexicalLy related verbs, 
tz'ansi t ire and inst\] a~it ire, indicating 
the :;ame phenomenon differently 
9. John ga t,4ary ni hon o m_!seta. 
",h)hn showed a hook to Mary. 
10. Mal'y ga hon o !~ita. 
"Uary saw a book.' 
The two expressions, or viewpoints, on the 
same phenomenon, that is, 'John showed to 
Mary a book which she saw.' are related 
in Japanese by the verb root ~_l. 
The system under consideration uti- 
lizes some of the above features (case 
marking and lexically related verbs) and 
in turn can be used to ease difficulties 
of automatic understanding, caused by some 
other features (case hiding, ambiguious 
case marking and deletion of noun 
phrases.) 
III. VERB CLASS 
The system is illustrated below with 
verbs related to the notion of movement. 
The verb classes in this category are as 
follows: 
(1) Verb class of causality of 
movementtCM) 
Examples:tsureteiku 'to take (a 
person)' 
tsuretekuru 'to bring (a 
person)" 
hakobu 'to carry" 
yaru 'to give" 
oshieru "to tell' 
Verbs of this class indicate that someone 
causes something or someone moves. How to 
move varies as seen later. 
(2) Verb class of movement(MV) 
Examples:iku "to go' 
kuru 'to come" 
idousuru "to move" 
Verbs of this class indicated that some- 
thing or someone moves from one place to 
another. 
(3) Verb class of existence(EX) 
Examples:iru '(animate) be" 
aru "(inanimate) be' 
Verbs of this class indicate the existence 
of something or someone. 
410 
(4) Verb class of possesslon(PS) 
Examples:motsu 'to possess' 
kau 'to keep' 
Verbs of this class indicate someone's 
possession of something or someone. 
the case slot. As seen below, the differ- 
ence between yaru, 'to give' and uru, 'to 
sell' is that the latter has 'money' as 
instrument, while the former does not. In- 
cidentally, Japanese has a verb yuzuru 
which can be used whether the instruh~ent 
Is money or not. 
Notice that the fundamental notion of 
MOVE here is much wider than the normal 
meaning of the word 'move'. When someone 
learns some idea from someone else. it is 
understood that an abstract notion moves 
from the former to the latter. 
IV. MAPPING OF SYNTACTIC STRUCTURES 
Furthermore, verbs of each class dif- 
fer slightly from each other in semantic 
structures. But the difference is de- 
scribed as difference in features filling 
Sentence 
I I I I i I I I I 
Agent Object Source Goal Instr Time Loc PRED 
I I I I t I I I 
B C O E F G HOVE 
Diagram l: Semantic Structure 
CV 
MV 
tsureteiku 
mottekuru 
hakobu 
ya ru 
uru 
oshi eru 
osowaru 
iku 
idousuru 
tsutawaru 
ta ke 
bring- Lo 
bring - for 
carry 
give 
sell 
tell 
learn 
SO 
move 
he conveyed 
Obj 
+ani 
-ani 
-ani 
÷ahs 
+a bs 
+abs 
Suppose sentences of the verb of MOVE 
have a semantic fram roughly as illus- 
trated in Diagram \]. 
The relationship among the surface 
A ga B o C kara D ni E de CI'I 
A ga B o C kara O ni E de MVsase 
B ga C kara D ni E de RV 
B ga C kara D ni E de CHrare 
B ga D n i EX 
D ga B o PS 
(sase and rare indicate causative and 
passive expressions respectively.) 
Diagram II:Mapping of Syntactic Structures 
Source Inst Goal 
+loc 
+loc 
+ani 
+loc 
+ant 
+hum 
+ani 
=~gt 
=~gt. 
=Agt 
=~gt 
+ant 
+ani 
=~gt 
=~gt 
=Agt 
=4gt 
-mone~' 
+money 
E× 
PS 
iru 
aru 
motsu 
kau 
be 
be 
have 
keep 
+ant 
-ant 
(-anim) 
+anim 
i I 
i 
................... _J 
OC 
o(' 
(ani, anim, h_.gum, abs and Ioc indicate animate, animal 
human, abstract and location, respectively) 
Diagram II1: ~erbs and conditions for realization 
411 
syntactic _~;tructures of the verb classes 
disc-usssed above is p\]'esented ill Diagram 
If. 
Items fill|rig the case slots in the 
semantic frame, or the nolln phrases in 
.qtlrf3c(" syntaclic 5~truclHFe.5. have partic- 
ular conditions depending on individual 
verbs. Some examples of (-ond i t i pry.; are 
presented in Diagram III. 
inference would be possible among sen- 
tences II through 14 in automatic text un- 
derstanding. Furthermore. this system can 
also be utilized in the automatic text 
understanding by locating missing noun 
phrases and determining ambiguous grammat- 
ical cases in the sentence, finding seman- 
tically related sentences between the 
questions and the text, and gathering the 
right semantic information. 
By the~ie conditions, the mapping of 
syntactic structures presented in Diagram 
II is transformed to that in terms of in- 
dividual verbs. Furthermore, rules of di- 
rections for reasoning presented in Dia- 
gram IV connect specific sentences. 
Take the following sentence for example. 
Since this system uses information on 
syntactic structures, it is much simpler 
in terms of the semantic structures than 
the Conceptual Dependencey Model, for in- 
stance, and the mapping among the sentence 
patterns semantically related much more 
explicit. 
II. John ga keiki o r,,lary ni mott ekita. 
(+ani) (-ani) (+ani} (CV-past) 
'John brought a cake for Mary.' 
has related sentences like the following. 
12. Keiki ga r~ary ni itta. 
"A cake went to t,4ary. 
13. Keiki ga ~,tary {no tokoro) ni aru. 
"There is a cake at Mary's" 
REFERENCE 
Fillmore. C. 1968. The case for case. IN 
E. Back and R. Harms (Eds.), Universals 
in linguistic theory. New York: Holt. 
Rinehart. and ~inston. 
Kusanagi, Yutaka et al. to appear. 
and Semantics 11 (in Japanese). 
Asakura Shoten. 
Syntax 
Tokyo: 
14. Mary ga keiki o motteiru. 
'Mary has cake. 
As far as air the rules and conditions are 
incorporated into the computer program. 
Schank. R.C.. and Abelson. R.P. 1977. 
Scripts, plans, goals, and under- 
standing. Hillsdate. N.J.: Lawrence 
Erlbaum. 
I) CM 
CM <==>CMrare 
CM <==>MV 
MVsa~_e<==>M~: 
MV <==>CMrare 
M~ ~ <==>PS 
2) MV - ->EX 
('V - ->EX 
MVsase -->EX 
('r'l raL,2 - - > PS 
~l~ - ->PS 
(%' - ->PS 
~IV sase - - > PS 
cV_r_:!r_~e - - • I'S 
<==>MVsase (The arrow indicates the direction 
for reasoning. 
== indicates that reasoning is 
possible anytime, and 
-- indicates that reasoning may 
be impossible if further 
information on MOVEMENT is 
is provided in the context.) 
Condition by Lense and aspect 
1) Same Lense and aspect on both 
of the arrow 
Per(fect).Past-->lmp(erfect).Non-Past 
2)Imp. Non-Past -->~on-Past 
Past -->Past 
Diagram I~" Direction and condition for reasoning 
I 
I 
412 
