HOW TO DEAL WITH AMBIGUITIES WHILE PARSING: EXAM --- 
A SEMANTIC PROCESSING SYSTEM FOR JAPANESE LANGUAGE 
Hidetosi Sirai 
Dept. of Mathematical Engineering and Instrumentation Physics 
Faculty of Engineering, University of Tokyo 
7-3-1, Hongo, Bunkyo-ku 
Tokyo 113, Japan 
It is difficult for a natural language understanding system (NLUS) to deal 
with ambiguities. There is a dilemma: an NLUS must be able to produce plausible 
interpretations for given sentences, avoiding the combinatorial explosion of 
possible interpretations. Furthermore, it is desirable for an NLUS to produce 
several interpretations if they are equally plausible. EXAM, the system described 
in this paper, is an experimental text understanding system designed to deal with 
ambiguities effectively and efficiently. 
I.INTRODUCTION 
What is ambiguity? The term 'ambiguity' 
usually refers to the property that certain sen- 
tences may be interpreted in more than one way 
and that insufficient clues are available for 
the intended or optimal interpretation.5 
The decision as to whether or not there are 
ambiguities in a given sentence is difficult; 
some systems with little knowledge may overlook 
the possibility of alternative interpretations in 
some sentences, and other systems may be puzzled 
as to which interpretation to choose even when 
there is only one plausible interpretation for 
human beings. 
In general, the more knowledge a system has, 
the greater are its possibilities of interpreta- 
tion. One solution is to generate all possible 
interpretations and ask the user to choose one 
of them. But this is obviously absurd. Another 
solution is as follows: in the parsing process, 
the system chooses one interpretation and if that 
one fails, looks for another, and in the semantic 
process it produces all plausible interpretations 
on the basis of certain criteria I0. 
A different approach is adopted in EXAM. 
The reasons for this are as follows: 
i) In the processing of the Japanese language, 
it is undesirable to separate the parsing and 
the semantic process, since noun phrases, espe- 
cially the subject are often omitted. Thus, we 
cannot parse Japanese sentences efficiently with- 
out using semantic information. 
2) It is undesirable to ask the user to choose 
from among several interpretations whenever ambi- 
guities occur in text understanding systems. 
3) We may overlook the possibility of other 
interpretations if we adopt the strategy of 
making semantic interpretations after parsing, 
since such a strategy might exclude what turns 
out to be the proper interpretation when making 
a choice among several grammatically ambiguous 
alternatives. It would be awkward if there is 
another interpretation and we realize that it is 
the appropriate one only after having processed 
several sentences. 
EXAM is an experimental text understanding 
system designed to deal with ambiguities effec- 
tively and efficiently. EXAM consists of three 
components: hierarchical knowledge sources, a 
semantic interpreter and a fundamentally breadth- 
first augmented context-free parser. The second 
version of EXAM is under development on the 
HLISP system using the HITAC 8700/8800 at the 
University of Tokyo. 
2. CLASSIFICATION OF AMBIGUITIES 
In this chapter, the ambiguities with which 
we are concerned are classified into three 
types: levels of word meaning, levels of grammar 
and levels of discourse. Examples are given for 
each of these types. We point out that these are 
among the ambiguities that an NLUS must deal with. 
2.1 Levels of word meanin$ 
There are many words in the Japanese lan- 
guage which are phonologically similar but cor- 
respond to different lexical entries in a dic- 
tionary. For example, sisei corresponds to 17 
entries in a standard Japanese dictionary 8. 
This is known as homonymy and should be distin- 
guished from polysemy. 
368 
The referents of pronouns are often ambi- 
guous. We call this ambiguity referential ambi- 
guity. In Japanese, nouns are often used coref- 
erentially, and so there may arise the problem of 
whether some noun is being used in the generic 
or in the specific sense. We also call this 
referential ambiguity. 
2.2 Levels of srammar 
Consider the following sentence. 
(i) Taroo wa Z~roo ni wazato nagura~se-ta. 
'Taroo made Ziroo beat him purposely.' 
This sentence is ambiguous, because there are 
two possible interpretations, namely, Taroo ga 
wazato nagura-se-ta 'Taroo purposely made...' 
and Z~roo ga wazato nagut-ta 'Ziroo purposely 
beat.' This is called grammatical ambiguity, 
that is, (i) has two different parsing trees ( 
or superficial syntactic structures), as follows: 
(l-a) \[Taroo ga Ziroo ni \[(Ziroo ga)(Taroo o) 
wazato nagura\] s se-ta\]s 
(l-b) \[Taroo ga Ziroo ni wazato \[(Ziroo ga) 
(Taroo o) nagura\] s se-ta\]s 
The ambiguity of coordinated noun phrases 
such as wakai otoko to onna 'young men and women' 
is also included in the grammatical ambiguity 
category. 
The interpretation of the scope of negatives 
such as (2) also constitutes a problem. 
(2) Taroo wa Ziroo no youni rikoude nai. 
This sentence is ambiguous, having the following 
three readings: 
(2-a) Taroo, like Ziroo, is not clever. 
(2-b) Taroo is not as clever as Ziroo. 
(2-c) Ziroo is clever, but Taroo is not. 
In Japanese, these three readings have the same 
superficial syntactic structure, but allow three 
different semantic analyses, according to the 
rule of interpretation used. We call this 
interpretative ambiguity. 
2.3 Levels of discourse 
The role of a sentence in discourse is often 
ambiguous. Consider the following sentence. 
(3) Onaka ga sui-ta. 
'I ~m hungry.' 
This is merely an assertion, with no special 
implied meaning when used without any particular 
context. But if we have a certain context, for 
example, if Taroo and Ziroo were walking along 
the street at noon, and Ziroo uttered sentence 
(3) in front of a restaurant, then (3) might 
have a different meaning, that is, "let's have 
lunch." (In this case, one says that (3) has 
illocutionary force.) Consider another example: 
(4) Taroo wa miti de koron-da. Banana no kawa 
ga soko n~ at-ta. 
'Taroo slipped and fell on the street. There 
was a banana skin there.' 
St is difficult to interpret the second sentence 
with assurance. We can interpret it as stating 
the cause of the event described by the first 
sentence, and also as stating a result of this 
event, that is, Taroo found the banana skin as a 
result of falling down in the street. We call 
this cohesion ambiguity. 
2.4 Sense and meaning 
In this paper, we distinguish 'sense' from 
'meaning.' "Meaning" refers to the interpreta- 
tion of linguistic expression, taking the context 
into account. "Sense" means, on the contrary, 
the interpretation of the expression in the ab- 
sence of context. In other words, "sense" is 
literal meaning. In this account a variety of 
ambiguities, such as homonymy and grammatical 
ambiguity, are regarded as ambiguities in sense. 
Other ambiguities, such as referential ambiguity 
and cohesion ambiguity are those in meaning. 
In EXAM, we adopt the strategy of first 
determining the sense of linguistic expression, 
and then the meaning. Therefore, the transfor- 
mation of sense representation into meaning rep- 
resentation i~ carried out after disambiguating 
the "sense" ambiguity. 
3. HOW TO DEAL WITH AMBIGUITIES IN PARSING 
EXAM incorporates three sources of knowl- 
edge: frame memory, text memory and working mem- 
ory. The frame memory holds prototype frames, 
script frames and other useful information such 
as metaknowledge. The text memory holds three 
components: the context, which is the so-called 
frame system connecting the frames produced by 
the semantic interpreter; the frame representing 
the sense of the last sentence (which constitutes 
the data for ellipsis), and the speaker's current 
view point. The working memory stores various 
interpretations of the sentence being processed 
during parsing. 
In parsing process, EXAM translates linguis- 
tic expression into frames Which represent their 
structure, such as word order. The transforma- 
tion of sense representation into meaning repre- 
sentation is not necessarily carried out during 
the parsing process but may be done if required. 
Constructing the sense representation from the 
expression depends upon the knowledge contained 
in the frame memory. EXAM also takes the knowl- 
edge contained in the text memory into consider- 
ation when transforming sense representations 
into meaning representations and selecting the 
plausible interpretations. 
The parsing process in EXAM is carried out 
by a parser called MELING and the knowledge repre- 
sentation language. In this chapter, we shall 
describe these and explain how EXAM deals with 
ambiguities. 
3.1 MELING --- a parser 
MELING stands for Modified Extended LINGol, 
which is slightly different from Extended LINGOL~ 
It is basically an augmented context-free parser 
using a bottom-up and top-down parsing algorithm 
369 
and parses input sentences by using the dictio- 
nary and the grammatical rules provided by the 
user. 
The descriptive format of the dictionary is, 
\[<morpheme> <syntactic-category> 
(<message-list> <interpretation>) <gen>\]. 
The <message-list> is regarded as attached to 
the syntactic category of the morpheme, and is 
used to control the parsing. <Interpretation>, 
when evaluated, returns the frames or other data 
relating to the appropriate function of the mor- 
pheme in the semantic processing. 
The format of the grammatical rules is 
\[<left> <right> (<advice> <cog>) <gen>\]. 
The <left>-<right> pair represents a context-free 
rule of the form 
A -÷ B or A -÷ B C, 
where A, B and C are non-terminal symbols. For 
example, 
\[S (NP VP) ((%S:NP+VP (FML)(FMR)) 
(#MM '@NP+VP=S (@LC)(@RC)) nil\] 
indicates that the phrase name is S and that its 
syntactic constituents are NP and VP. 
In general, it is possible that several 
parsing trees may be produced for one sentence, 
therefore, the parsing process must be con- 
trolled by using syntactic and semantic informa- 
tion. The <advice> and the <cog> are provided 
for this purpose. 
The <advice> is an arbitrary LISP function 
which serves to control the parsing process by 
using syntactic information. It is evaluated if 
the parser creates a new requirement for a pars- 
ing tree in a top-d0wn manner by using the gram- 
matical rule under consideration, or if the rule 
is applied to produce a new parsing tree in a 
bottom-up manner; the parsing process depends 
upon the result. The <advice> program should be 
one which returns the result deterministicalSy 
in the local syntactic context. For example, a 
program dealing with the inflection of verbs is 
provided to serve as <advice> for the rewriting 
rule which decomposes a verb into its root and 
its suffix. 
The <cog> is a LISP S-expression (or pro- 
gram) for producing interpretations and con- 
trolling the parsing process in terms of these 
interpretations. Usually, semantic processing 
costs more than syntactic processing, hence the 
parser does not evaluate the <cog> very frequent- 
ly. The <cog> would be evaluated in the follow- 
ing cases: 
i) if several partial parsed trees with the same 
root node and the same terminal symbols (Fig. i; 
we call these ambiguous PPTs) were found to make 
sense given the syntactic context, or 
2) if some phrases are produced which are con- 
sidered as components for semantic interpretation 
(e.g. sentence, thematic phrase, adverbial clause, 
etc.) 
noun 
adj ...... noun 
l ! 
! det---noun 
! noun .... p ! 
! ! ! f 
kuroi kcmTi no syouzyo 
noun 
I 
det .... noun 
noun ..... p \] 
adj---noun ! i 
! ! ! ! 
kuroi kcmzi no syo~zyo 
'a girl with dark hair' 
Fig. i An example of ambiguous PPTs. 
The result of such an evaluation is a list 
of pairs each consisting of an interpretation 
and a number which indicates to the parser the 
degree of our satisfaction with the interpreta- 
tion (or "likelihood"). 
As we have seen, syntactic and semantic 
processing are distinctly separated, and the 
semantic processing is dependent upon the parsing 
tree. Furthermore, if there are grammatical am- 
biguities, that is, ambiguous PPTs, the semantic 
processer is used to retain only plausible inter- 
pretations, and then MELING continues the parsing 
process accordingly. We pointed out that al- 
though there may be several semantic interpreta- 
tions, the number of parsing trees is just one. 
That is, the interpretations produced all belong 
to the same syntactic category. Thus, MELING 
eliminates the ambiguous PPTs. 
3.2 GIRL --- a knowledge representation languase 
GIRL has frame-like knowledge packets as a 
basic data type, and these packets are mutually 
connected by certain links. Following KRL 1 ter- 
minology, the packets are called units, and the 
links are called slots. The format of a unit is: 
\[unit-name category 
self-slot 
slotl slot2 ... slotn\]. 
In EXAM, interpretation may be regarded as 
transforming protytype frames into frames repre- 
senting the meaning of the phrases or sentences 
by instantiating them. The prototype frame ( 
called PROTO-unit) is indicated by the category 
"PROTO" in the frame memory. GIRL provides a 
hierarchy with several modes of property inher- 
itance for PROTO-units, and uses this hierarchy 
to instantiate them in the semantic interpreta- 
tion. The units have several slots, and most of 
them have the following format: 
(role facet filler comment) or 
(role check-to-fill when-filled comment). 
The former is called a semantic slot and 
the latter is called a prototype slot. The 
role/filler pair in the semantic slot corresponds 
to the traditional attribute/value pair. The 
facet specifies the characteristic of filler; it 
is usually "=" (value). The comment is used for 
various purposes: default value, expectation, 
attached procedures to be evaluated later, etc. 
are specified. 
EXAM instantiates PROTO-units by transform- 
- 370 
ing their prototype slots into semantic slots. 
In addition to this, several slots are added and 
removed. In instantiating units, the check-to- 
fill is evaluated when a candidate for the filler 
of the slot is provided, and returns either NIL 
or a number as the result. If the result is NIL, 
then the candidate is abandoned. Otherwise, the 
result, that is, a number, indicates the candi- 
date's fitness and then the transformation from 
prototype slot into semantic slot is carried out. 
After this is comleted, the when-filled is eval- 
uated. The when-filled may consist of any kind 
of programs. 
GIRL has another type of slot which speci- 
fies the definitions of the units and the global 
requirements applicable to the unit as a whole. 
However, we shall not describe it here. 
3.3 Ambiguities and interpretation 
As we have seen, the semantic processing is 
dependent upon the parsing tree. In more con- 
crete terms, the interpretations, that is, frames, 
of a certain phrase consist of the interpreta- 
tions of its syntactic constituents. In case 
the form of a grammatical rule is A + B C, the 
frames of the constituents B and C form the ar- 
guments for the <cog> of the grammatical rule, 
and then the semantic interpreter produces the 
interpretations of the phrase A. In this manner, 
the semantic processing is carried out in a 
bottom-up and left-to-right fashion. 
Here we have a problem: since MELING is a 
basically breadth-first parser, the number of 
interpretations which are senseless to human 
beings becomes very large as the length of the 
sentences increases, and the parser operates 
inefficiently. (In fact, the time required for 
the older version of MELING to process a sentence 
of length n is generally proportional to n 2 and 
sometimes n~.) 
However, EXAM does not produce all combina- 
tions of possible interpretations. The semantic 
interpreter is evoked in two cases (see 3.1): in 
the first case, it determines the sense represen- 
tation and eliminates some interpretations; in 
the second case, EXAM attempts to produce the 
meaning representation and also produces certain 
"anticipations". 
In the first semantic interpretation, the 
traditional and most powerful tool is so-called 
semantic marker. EXAM implements this tool using 
the generalization hierarchy. For some "cases", 
such as locative and time, the semantic marker 
functions as the selection restrictions. However, 
for other "cases" it does not, and in such cases, 
it is regarded as an indicator of the "likeli- 
hood" of various interpretations. Furthermore, 
the Japanese language has many homonyms which 
have the same categories, especially nouns. We 
group such homonyms into several special frames 
whose category is "GROUP" in accordance with 
their semantic category. If there is sufficient 
information to determine the sense of the seman- 
tic interpretation, the "GROUP" frames are re- 
placed by the frames corresponding to the appro- 
priate sense. 
The "case ordering" is also an indicator of 
"likelihood". Inoue 4 points out that in Japanese, 
there is a certain ordering of cases in themati- 
zation and relativization, such as: 
subject>object>dative>locativeegoal>source... 
We have adopted this notion and have applied it 
to the disambiguation of certain grammatical 
ambiguities. For example, consider the following 
phrase: 
Taroo no kenkyuu 
In the absence of any particular context, the 
system is more likely to interpret this phrase 
as Taroo ga suru/sita kenkyuu 'the research 
carried out by Taroo' than Taroo ni tuite no 
kenkyuu 'research concerning Taroo'. 
These'devices are very effective in dealing 
with homonymy, polysemy and grammatical ambi- 
guity. EXAM chooses the most plausible interpe- 
tations depending upon their "likelihoods". If 
there are several interpretations whose "likeli- 
hoods" are equally great, then EXAM retains all 
of them. That is, "likelihood" is used to indi- 
cate the order of preference of interpretations. 
In the semantic interpretation, if the 
"PROTO" frames are instantiated with some slot 
filler, then the category "PROTO" is replaced 
by the category "INSTANT". The distinction 
between these categories, that is, "PROTO" and 
"INSTANT", is important. For example, akai iro 
no kuruma 'a red car' and sonna iro no kuruma 
'a car with such a color' are well-formed expres- 
sions, but iro no ku~ma 'a car with color' is 
rather ill-formed. We explain this phenomenon 
as follows: the frame with the "INSTANT" category 
is, as it were, a specified frame, and is there- 
fore preferred to "PROTO" frames in the modifi- 
cation of other frames. This distinction is also 
used as an indicator of "likelihood". 
\[We must note that the frames with catego- 
ries such as "PROTO", "INSTANT" and "GROUP" are 
merely temporary in the working memory, and these 
categories are replaced by appropriate ones such 
as "CLASS" (which means generic), "INDIVIDUAL" 
(which means specific object) or "SOME" (which 
means indefinite object) in the meaning inter- 
pretation process.\] 
In the second semantic interpretation, 
dealing with referential ambiguity constitutes 
the most important phase of the process. Usuall~ 
candidates for the referent are not uniquely 
determined in the local context where the ambi- 
guity occurs. Therefore, EXAM delays the dis- 
ambiguation until after the entire sentence has 
been processed. EXAM collects the requirements 
of the referent from the interpretation of the 
comlete sentence. In particular, some Predicates 
such as hosii 'want' produce opaque contexts, 
and in such cases the category determination II 
should be carried out after processing the entire 
sentence. 
--371 .... 
Another 
pretation is 
parsed trees 
ing sentence 
(5) Taroo no 
task involved in the second inter- 
the elimination of unnecessary 
. For example, consider the follow- 
inu wa korii da. 
'Taroo's dog is a collie.' 
When MELING has processed the sentence (5) up 
through the word wa, it produces the following 
partial parsed trees: 
S nOUFI 
theme \ \ 
det theme 
Taroo no inu wa Taroo no inu wa 
In this case, only the first is plausible, and 
the second is unnecessary. (In fact, the second 
partial parsed tree is plausible only when the 
sentence is of a form such as: Taroo no, inu wa 
...) Therefore, EXAM eliminates unnecessary 
parsing trees in accordance with the result of 
the semantic interpretation, that is, "likeli- 
hood". This method makes the parsing process 
more efficient, but involves some risk of mis- 
interpretation. Hence, the elimination of pars- 
ing trees is carried out on the basis of certain 
linguistic evidence 7, and the number of the 
parsed trees which are retained may sometimes be 
greater than one. 
The other task is to produce "anticipations" 
in the second semantic interpretation. Consider 
the following sentence: 
(6) Taroo wa terebi o mi-nagara benkyousi-ta. 
'Taroo studied while watching television.' 
When EXAM observes nagara, it anticipates that 
the agent of mi(ru) 'watch' will be coreferential 
with the agent of the verb of the matrix sen- 
tence, that is, in this example, benkyousi 
'study'. In this manner, "anticipations" also 
serve to produce plausible interpretations and 
eliminate some ambiguities. 
As for ambiguities in meaning, EX~ does 
not deal with these in an entirely conclusive 
manner. However, we should note that "cohesion 
relations ''3 play an important role in disambi- 
guation. Dealing with ambiguities in meaning 
requires comprehension of the structure of the 
text and the determination of the topic. The 
structure of the text is determined by the cohe- 
sion relations. First, EXAM attempts to recog- 
nize how sentences are related. In particular, 
conjunctions and sentences which are provided to 
support the reader's comprehension are employed 
in the recognition of these relations. If EXAM 
succeeds in recognizing these relations, then 
the inference mechanism attempts to explain why 
these sentences are related. In this manner, 
EXAM deals with ellipsis and properly disambi- 
guates some sentences. For example, 
(7) Kaeru wa hiatari no yoi mizu no naka ni 
t~nago o umi-masu. Atatakai tokoro no hou ga 
yoku sodatu kara desu. 
'Frogs lay their eggs in water which is well 
exposed to sunshine. Because they grow well 
in warm places.' 
The second sentence is related to the first by 
the "explanation relation" and this is indicated 
by kara 'because'. Then EXAM attempts to clarify 
the question of why the second sentence consti- 
tutes an explanation of the first. In this case, 
mizu no naka 'in water' and tokoro 'place' corre- 
spond to one another, and EXAM discovers the 
omitted element (kaeru ga un-da) tamago 'eggs 
(which are layed by frogs)' 
4. DEALING WITH AMBIGUITIES AFTER PARSING 
So far, we have discussed the question of 
dealing with ambiguities while parsing. However, 
ambiguities may still remain! Furthermore, in 
some sentences, such as the headings of news 
articles, we often find ambiguities which we 
cannot eliminate for lack of a preceding context. 
How can we deal with such situations? In this 
section, we describe some strategies which are 
still under development now. 
In such cases, we must delay the disambi- 
guation and provide some mechanism for selecting 
the appropriate interpretation when sufficient 
information has been supplied. One solution is 
to introduce a kind of Truth Maintenance System2. 
For example, consider the following sentence: 
(8) Every man loves a woman. 
As is well known, sentence (8) has two interpre- 
tations. These interpretations are stated in 
the first order predicate calculus as follows: 
(8-a) Vx\[man(x) + my\[woman(y) A love(x,y)\]\] 
(8-b) 3y\[woman(y) ^ Yx\[man(x) ÷ love(x,y)\]\] 
This sentence should not be regarded as meaning- 
less, even if we have no context and hence can 
not disambiguate it. Since we can deduce (8-a) 
from (8-b), we surely have at least the informa- 
tion described in (8-a). Hence, we enter (8-a) 
into the text memory as a "premise" and (8-b) as 
a "hypothesis". If some information contradict- 
ing (8-b) exists, the Truth Maintenance System 
will delete (8-b) from the text memory. 
Here, we adopt the following standpoint: if 
the disambiguation is essential for the under- 
standing of the text, the text (or the writer) 
will certainly provide clues adequate to dis- 
ambiguate the sentence, and if this is not the 
case, the system may adopt any interpretation 
consistent with the context. 
In fact, sentences which follow ambiguous 
ones are often paraphrases of or explanations 
for them. For example, 
(9) John says that everyone loves a woman. Whom 
does John think everyone loves? 
The first sentence of (9) is ambiguous, but we 
can disambiguate it by means of the second. 
Thus, we had better delay the interpretation of 
ambiguous sentences after having processed 
372 
several subsequent sentences. 
5. CONCLUSION 
We have discussed the procedures by which 
EXAM deals with ambiguities. This constitutes 
a difficult task for an NLUS and the trivial 
method of asking the user to choose one of sever- 
al possible interpretations has been adopted. 
In dealing with ambiguities, EXAM avoids the 
combinatorial explosion of possible interpreta- 
tions by means of several devices. We classify 
ambiguities into two categories, that is, ambi- 
guity in sense and ambiguity in meaning. EXAM 
adopts the strategy of first processing sense 
representations, and secondly meaning represen- 
tations; the parsing process is carried out in 
an essentially breadth-first manner. 
However, EXAM does not completely clarify 
ambiguities in meaning, especially ambiguities 
which are not resolved by the preceding context. 
This constitutes a problem which still awaits 
solution. 
ACKNOWLEDGEMENT 
The author wishes to express his sincere 
gratitude to Professor Masao Iri, Dr. Hozumi 
Tanaka, Dr. Elmer J. Brody and Hidetosi Yokoo 
for encouragement, cooperation, and various 
useful comments and suggestions. 

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