Detecting Japanese Term Variation in Textual Corpus
Fuyuki Yoshikane, Keita Tsuji(Graduate School of Education, University of Tokyo, Japan),
Kyo Kageura(National Center for Science Information Systems (NACSIS), Japan),and Christian Jacquemin(Laboratoire d'Informatique pour la Mecaniqueet les Sciences de l'Ingenieur (LIMSI-CNRS), France)

Abstract

jority (Ishii, 1987)1 . The ratio of multi-word terms intechnical terminology in general is growing, so multiword terms are expected to play an important role in
information retrieval (IR).

In this paper, we describe a rule-based mechanismthat detects Japanese term variations from textual
corpora. The system operates on the basis of metarules that map syntactic and morpho-syntactic variations of terms to the original forms of terms. The
framework used here has been successfully applied insuch languages as English and French, and we show
here that this also works well in detecting Japaneseterm variants, once we properly take into account specic characteristics of Japanese language. We also
discuss the potential of this work for IR related applications.

However, multi-word terms do not always appear in
text in their canonical forms. Terms tend to be syntactically and/or morphologically transformed into
their variants when appearing in an actual context,
while keeping the identity of concepts they represent.
By detecting those variants properly, therefore, we
can expect an improvement of recall in retrieval, without a ecting precision.
Fastr (Jacquemin, 1994) is a system which was developed for the recognition of term variants in textual corpora and has been successfully applied to suchlanguages as French and English (Jacquemin, 1996
Jacquemin, Klavans, & Tzoukermann, 1997). In thispaper, we report the preliminary results of applying
the same framework to Japanese term variant detection, i.e. the use of Fastr for Japanese term variant
detection.

1 Introduction
Recently the role of technical terminology in communication has become more and more important, notonly in limited technical communication but also in
more general communication. Among scientic terminology, complex or multi-word terms consisting of two
or more elements or single words constitute the ma-

Here we use multi-word terms and complex terms as synonyms, although they are not exactly the same.
1

1

term.
Meta rules, which take the form of context-freeskeletons, dene the syntactic structures of originalterms and their potential variants. The following is
an example of a meta-rule:

We briey summarise the general characteristics ofthe Fastr system in section 2. In section 3, we describe
the Japanese rules for detecting term variants. In
section 4, the results of a preliminary experiment in
detecting term variants using Fastr is reported.

Meta-rule Coordination (X1 ! X2 N3 )= (X1 ! X2 N5 C4 N3)

2 General Framework for Term
Variant Detection using Fastr

The left side of the equation represents the structureof original terms, and the right side represents the associated variations. Here N represents a noun, C represents a coordinating conjunction, and X represents
any syntactic category. The same subscript on theleft and right side of the equation shows that theseelements are the same. So this meta rule indicatesthat a complex term which consists of an element ofan arbitrary category followed by a noun may have avariation whose structure is the rst element of theoriginal term followed by the coordinating conjunction of a noun and the noun element in the originalterm.
Assume that we have dened a term rule of \language processing" as described above. Then, by applying the term rule to the meta rule `Coordination',
Fastr generates a new rule:

In this section, we describe the framework for term
variant detection using Fastr, a unication-based partial parser aimed at detecting term variants in texts(Jacquemin, 1994), which we used for the Japaneseterm variant detection reported in this paper. In the
Fastr framework, one has to describe the linguisticknowledge and rules for detecting term variants using three types of rules, i.e. (1) meta-rules which
describe the skeleton of variation patterns, (2) term
rules which list and describe the structure of originalterms whose variations are to be detected, and (3)single-word rules which dene morphemes or singlewords used in original terms. For instance, a term
rule of \language processing" and single-word rulesof constituent words of the term, i.e. \language" and\processing", are described as follows.
Rule (N1 ! N2 N3 )< N2 lemma> = `language'< N3 lemma> = `processing'Rule (X1 ! language processing)= (X1 ! language N5 C4 processing)
By this rule, Fastr can detect terms such as \language
understanding or processing" as as a variant of \language processing".
Fastr can recognise not only syntactic transformations but also morphological transformations. This isdone by adding constraint equations, which link morphological derivatives to meta-rules. For instance, thefollowing meta-rule generates a variant including Ad-

Word `language' :<cat> = N.
Word `processing' :<cat> = N.
By applying term rules such as this to meta rules,
Fastr generates new rules which detect variants of the2

Firstly, V S , the s-inexional or sahen verb suru (`do'),is distinguished from common verbs (V ) because thesahen-verb functions as both a common and special
auxiliary verb2 . Sahen-nouns (NS ), which can beturned into verbs by adding the sahen-verb to theend are distinguished from other nouns (N ) as theirsyntactic behaviour is di erent.

jective (A4 ) which is a derivative of Noun (N2 ).
Meta-rule Noun-Adjective(X1 ! N2 N3 )= (X1 ! A4 N3 )< A4 root> = < N2 root>
By this meta-rule, Fastr detects, for instance, \categorial grammar" as a variant of \category grammar"
because \categorial" and \category" share the sameroot.

S , `postpositional particle connecting coordinate
words', corresponds to English coordinating conjunctions such as \and" and \or". We distinguished themfrom the other postpositional particles because this isnecessary in order to describe an important variation
pattern of Japanese terms.

3 Japanese Meta-Rules
3.1 Basic Syntactic Categories
The syntactic and morphological structures ofJapanese are di erent from those of languages suchas English or French. For instance, there is no delimiting symbol between words, and it is not possible
to formally distinguish derivation from compounding(cf. Koyama, Yoshioka & Kageura, 1998). As our aim
is to develop a practically applicable system, we decided to follow the convention of word-boundary delimitation and broad-level syntactic categories usedin the most widely used morphological analyser forJapanese, i.e. JUMAN (Matsumoto, et. al, 1998).
Word categories must be dened accurately inmeta-rules as well as in term and single-word rules.
For this purpose, we have also used some detailedlevel categories used by JUMAN in some cases. All
in all, 20 syntactic categories were taken from the JUMAN tagset.
The basic syntactic categories used in describing
Japanese rules for term variant detection are given in
Table 1. Categories of functional words which are notused in describing Fastr rules are not listed in Table
1.
Some categories in Table 1 need explanation.

Suxes which are relevant to describing term variations are divided into three. TPNN is the sux that
produces the noun or adjectival stem from the noun,
and the majority are one-Chinese character suxes(Nomura, 1978 Kageura, 1994). TPAN is the sufx that produces the adjective from the noun. Othersuxes are categorised into one group. As we will
discuss shortly, this distinction presupposes the decomposition of JUMAN, which is practically useful
but not necessarily ideal from the theoretical point ofview. Note that suxes are regarded here as independent units, just like other categories for independent words. This comes from the characteristic useof Chinese characters in Japanese, and will make thedistinction of syntactic and morpho-syntactic variations as dened in the Fastr framework less clear indening meta-rules.

The sahen verb or s-inexional verb is referred to as such
because the inexional paradigm is bound to such patterns as`sa', `si', `su', `se', etc.
23

Symbol Category

L
V
VS
A
N
NS
RB
S
SC
TP
TPNN
TPAN

Delimiter
Verb (except V S )Sahen-verb suru
Adjective
Noun (except NS )Sahen-noun
Adverb
Postpositional particle (except SC )Postpositional particle connecting coordinate words
Sux (except TPNN and TPAN )Sux deriving noun from nounSux deriving adjective from noun

Table 1: Major categories in the part-of-speech scheme

3.2 Patterns of Japanese term variations

this stage, only verbs, adjectives, nouns and adverbs
were checked. After examining the data, we dened
the following patterns.

In order to dene the meta-rules (or the linguistic
rules) that can describe term variants, we observed
actual variations of terms that occur in texts. Firstly,
we prepared terminological and textual data. The terminological data were extracted from the terminological part of the EDR database, which lists information
processing terms (EDR, 1996). Among the approximately 120,000 EDR terminological data, we sampled
about 40,000 terms. For textual data, we used 1827
titles and abstracts of conference presentations in theeld of articial intelligence, a subdomain of the eldof information processing, extracted from the NACSIS Academic Conference Database (NACSIS, 1998).

A. Modication :Insertion of a modier makes variants, whichhave narrower or more informative senses thanthe original terms. Nouns, adjectives and verbs
are used for modication of nouns. However,
only nouns can be inserted between nouns as amodier without a postpositional particle.
For example,
zenbun kensaku ! zenbun kiiwaado kensaku (fulltext search ! full text key word search)

B. Decompounding/Compounding :A compound noun can be decompounded by inserting a postpositional particle. Such a decompounded term is a variant of the original compound noun. In some decompounding variations,the appropriate verb is inserted together with thecase-marking postpositional particle such as `wo'.

Both terminological and textual data were processed by JUMAN 3.5. For each multi-word term in
the terminological data, we extracted corresponding
sentences from the textual data, i.e. sentences which
include all the constituent elements of the term. At
4from the sahen-nouns. We call the variations
relating to such types of derivation sahen-nounverb variations.
For example,
gainen gakushuu $ gainen wo gakushuu suru
(concept learning $ learn concept)

For convenience, the variation in which a delimiter is inserted between nouns is classied into
this pattern3 .
In the compounding variation, on the other hand,the postpositional particle or the delimiter between nouns is omitted.
For example,
jouhou kensaku $ jouhou no kensaku (information retrieval $ retrieval of information)
fureemu waaku $ fureemu - waaku (frame work)
kumiawase mondai ! kumiawase wo motomeru
mondai (combination problem ! combination
seeking problem)

E. Noun-Noun variations :Some nouns can be syntactically transformed
into their noun derivatives or adjectival stems byadding special suxes, e.g. teki, ka, etc. Let us
conveniently call this type of variation the nounnoun variation.
For example,
kika moderu $ kika teki moderu (geometry
model $ geometrical model)

C. Coordination :Terms which consist of two coordinated elementswith a common head word or a common argument are considered to be variants of the original terms which consist of one of the coordinatedelements with the head or the argument. The
variation combining two terms with a common
sahen-verb suru is also classied into this pattern.
For example,
bunkai genri ! bunkai -ketsugou genri (resolution theory ! resolution and combination theory)chishiki no kakutoku ! chishiki no seisei ya
kakutoku (knowledge acquisition ! knowledge
generation and acquisition)hikaku suru ! hikaku - kentou suru (compare !
compare and examine)

F. Na -Adjective-Noun variations :By eliminating the inexion of a na -adjective, we
can morphologically transform the adjective into
a noun. Likewise, by attaching the inexion to
the noun which is the root of the na -adjective, we
can morphologically transform the noun into an
adjective. The inexion na is not an independentmorpheme but a part of na -adjective. Therefore, na -adjective-noun variations are morphosyntactic ones.
For example,
aimai jouhou $ aimaina jouhou (ambiguous information)

G. Noun-Adjective variations :The Sahen-verb suru can be connected withsahen-nouns and constitute various verbs derived

Some nouns are syntactically transformed into
adjectives by adding suxes which can derive
adjectives from nouns. We call the variations relating to such types of derivation noun-adjective
variations4 .

In Japanese, loan terms may or may not be divided intowords by a delimiter.

In fact, this can be dened as the combination of meta-rule
types D and G. We established this type because for now we

D. Sahen-Noun-Verb variations :

3

4

5

For example,
kika moderu $ kika tekina moderu (geometry
model $ geometrical model)

suu " is the composition of the na -adjective-noun variant \dokuritsuna hensuu ! dokuritsu hensuu " andthe sahen-noun-verb variant \dokuritsu hensuu !
dokuritsu sita hensuu ".

Many of the patterns of Japanese term variations
above are analogous to those in French or English(cf. Jacquemin, Klavans, & Tzoukermann, 1997). In
such languages as English or French, it is relatively
easy and straightforward to distinguish derivation andcompounding. In Japanese, however, this is not thecase. Linguistically speaking, it may be more appropriate to regard the variations E, F and G as variations based on morphological operations.
However, in implementing these meta-rules in Fastr(Jacquemin, 1994), only F is categorised as a puremorpho-syntactic variation in contrast to syntactic
variations. In morpho-syntactic variants, at least oneof the content words W of the original term is transformed into another word W' in the variant such that
W and W' have the same root form. However, as
there are ambiguities between syntactic and morphosyntactic variations especially in the case of E, whichis caused by the ambivalent status of the Japanesesux, G and F are dened in the same manner as
syntactic variations here.
On the other hand, that A, B, C and D are syntactic variations is reasonably clear. We do not go into
this further in this paper as this is more closely related to Japanese morphological analysis than to term
variant detection. What should be emphasised hereis that the borderline between syntactic and morphosyntactic variations is less clear than in the case ofEnglish or French.
In some cases, variants are produced by combinations of these elementary patterns. For instance, thevariant \dokuritsuna hensuu ! dokuritsu sita hen-

Table 2 shows sample meta-rules for each type ofvariation. These meta-rules consist of context-freeskeletons and constraint equations. The symbols `*'and `+' in context-free skeletons are regular expressions, expressing their well-established meaning, i.e.,`*' means `zero or more times' and `+' means `one ormore times'. The tags used in constraint equations
are based on the form of Fastr.

4 Experiments and Evaluations
After having established these meta-rules forJapanese term variants, we carried out a preliminary
experiment to check the validity of the meta-rules.
As mentioned previously, Fastr has to be equippedwith meta-rules, single-word rules and term rules.
We compiled about 87,000 term rules from EDR
terminological data in information processing5 , andabout 17,000 single-word rules derived from their constituent elements.
In order to evaluate the meta-rules described in theprevious section, we applied them to a corpus consisting of the titles and abstracts of 914 papers, half thedata belonging to the eld of articial intelligence.
The number of sentences in the corpus was 5,322. By
JUMAN 3.5, those sentences were decomposed into
150,406 words.
From 120,000 original terms, we excluded extremely longelements, which cannot be processed by Fastr, as well as borrowed elements without delimiters when their variants withdelimiters are also listed.
5

cannot control the application order of meta-rules.

6

Variation
A. Modication

Sample meta-rule, and example of transformation

X2 X3 ! X2 (N j NS )+ X3< X2 cat> = N j NS , < X3 cat> = N j NS

zenbun N] kensaku NS] ! zenbun N] kiiwaado N] kensaku NS](full text search ! full text key word search)

B. Decomposition

B. Composition

C. Coordination

X2 X3 ! X2 S4 A? (N j NS ) X3
< X2 cat> = N j NS , < X3 cat> = N j NS , < S4 lem> = `no 'shisutemu N] tokuchou N] ! shisutemu N] no S] omona A] tokuchou N](system feature ! main feature of system)
X2 S3 X4 ! X2 (N j NS ) X4
< X2 cat> = N j NS , < X4 cat> = N j NS , < S3 lem> = `no 'jouhou N] no S] kensaku NS] ! jouhou N] kensaku NS](retrieval of information ! information retrieval)

X2 X3 ! X2 S4 A? (N j NS )+ (SC j L) X3< X2 cat> = N j NS , < X3 cat> = N j NS , < S4 lem> = `no 'chishiki N] kakutoku NS] ! chishiki N] no S] seisei NS] ya SC] kakutoku NS](knowledge acquisition ! knowledge generation and acquisition)

D. Sahen-noun-verb

X2 NS3 ! X2 S4 (A j RB ) NS 3 V S5
< X2 cat> = N j NS , < S4 lem> = `wo 'gainen N] gakushuu NS] ! gainen N] wo S] gakushuu NS] suru VS](concept learning ! learn concept)

E. Noun-Noun

X2 X3 ! X2 S4 A? X3 TPNN5< X2 cat> = N j NS , < X3 cat> = N j NS , < S4 lem> = `no 'gainen N] kaisou N] ! gainen N] no S] kaisou N] ka TPNN](concept hierarchy ! hierarchisation of concept)

F. Na -adjective-noun A2 X3 ! X4 (N j NS ) X3
< X3 cat> = N j NS , < X4 cat> = N j NS , < A2 root> = < X4 root>
aimaina A] jouhou N] ! aimai N] jouhou N] (ambiguous information)
G. Noun-Adjective
X2 X3 ! X2 TPNN4 A? (N j NS ) X3
< X2 cat> = N j NS , < X3 cat> = N j NS
kika N] moderu N] ! kika N] tekina TPAN] moderu N](geometry model ! geometrical model)

Table 2: Japanese meta-rules

74.1 Basic Results of Preliminary Experiments

constrained.

X2 X3 ! X2 X4 X3< X4 cat> = TP j TPNN j TPANX2 X3 X4 ! X2 X4< X3 cat> = TP j TPNN j TPAN

After applying Fastr based on the above-mentioned
meta-rules, term-rules and single-word rules, we manually judged the result. If the content-bearing elements of an original term appear in its variant withthe relation, the concept represented by the originalterm will be kept in the variant even if the surface
form is changed. We judged such a variant as correct.
The number of original terms extracted from thecorpus was 5,164. The number of extracted variants
are shown in Table 3. Table 3 shows the numbers ofcorrect and incorrect variants and the precision, i.e.
the ratio of correct variants to extracted variants.
The total precision of rules from A to G exceeded
90%. As for individual rules, the precision of C was
65%, but other than that, the precision of all the rules
were well over 90%. As we will see shortly, further
improvement is expected to be possible with respect
to C.
In addition to the `proper' meta-rules dened in theprevious section, we also tried some rough meta-rules,
to observe possible variations not covered by rules A{
G, and also to observe the eect of the granularity ofrule descriptions.

gengo N] tekida TPAN] $ gengo N] gaku TPNN]
tekida TPAN] (linguistic)

X3. Permutation of nouns.

X2 X3 ! X3 (N j NS ) X2
< X2 cat> = N j NS , < X3 cat> = N j NS

memori N] kyoyu NS] ! kyoyu NS] waakingu N]
memori NS] (memory sharing ! shared working
memory)The precision of the rule X is very low, i.e. less than
60%. If we consider X together with proper metarules, the overall precision is less than 85%.

4.2 Usefulness in Indexing
In order to assess the important role of term variation in automatic indexing, we have also evaluatedthe result of regarding the experiment as automatic
indexing over a controlled vocabulary. Our purpose
was to evaluate to which extent the exploitation ofvariant conation increases the coverage of indexing.
For the same 914 documents with 87,000 terms, we
compared the number of indexed documents with andwithout taking into consideration variant forms. The
statistics are shown in Table 4. If we use variants ofterms in indexing documents, we can nd, on average,
about two additional papers per term in the corpus6 .
RAP in Table 4 indicates the ratio of the papers
indexed by variants but not by the original terms to

X. Others :X1. General insertion or omission of postpositional particle. Categories of both the left andright words are not constrained.

X2 X3 ! X2 S4 X3
X2 S3 X4 ! X2 X4hikaku NS] suru VS]
suru VS] (compare)$ hikaku NS] wo S]

X2. General insertion or omission of sux. Cat-

Here, we only consider the terms whose variants appear in
the corpus at least once.
6

egories of both the left and right words are not
8Variation

Correct Incorrect Sum Precision

A. Modication
B. Decompounding / Compounding
C. Coordination
D. Sahen-noun-verb
E. Noun-noun
F. Na -adjective-noun
G. Noun-adjective

366
755
51
180
39
8
37

27
51
29
16
3
0
1

393
806
80
196
42
8
38

93.13 %
93.67 %
63.75 %
91.84 %
92.86 %
100.00 %97.37 %

Sum(A-G)

1436

127

1563

91.87 %

X. others

260

178

438

59.36 %

Total sum(A-X)

1696

305

2001

84.76 %

Table 3: Extracted variations

4.3 Diagnosis

applied, but the same type of errors are often observed in variants extracted through types B-G, because these types incorporate the same operation asmodication (see Table 2). This type of error might
be avoided by rening the system of syntactic categories. Another possibility for improving the performance is to take into account the external context
surrounding the elements described by meta-rules.

In the above preliminary experiment, we noted a few
points related to meta-rules, which we briey discuss
here. There are two notable errors related to themeta-rules A to G.
Firstly, insertion of words sometimes changes therelationship between constituent elements of theterms. This accounts for many incorrect variants
extracted through proper meta-rules. For example,
gakushuu shien shisutemu (learning support system)
is an incorrect variant of gakushuu shisutemu (learning system), as `learner' is the `system' in the caseof the original term, while `learner' is people not the`system' in the variant. This type of error typicallyoccurs when the type A (modication) meta-rule is

Secondly, the performance of type C (coordination)
was proved to be very low compared to other `proper'
meta-rules. Most of the incorrect variants detectedby rule C were caused by incorrect extraction of themodication structure. For example, kioku no ] kousei oyobi taiwa no seigyo (organisation of memory]and control of dialog) may not be a correct variantof kousei seigyo (organisation control) because kouseiin the variant does not modify seigyo. This is related
to the limitation of the formal mechanism of Fastr,
i.e. it cannot take into account the full structure ofphrases or sentences, but still some of these errors
may be avoided by the renement of meta-rules andsyntactic categories. For instance, we can expect thatthe papers indexed by the original terms. As for theterms which appear in the corpus in both their original forms and variants, the number of additional papers indexed by variants is, on average, approximately
equal to the number of papers indexed by the original
term.

9

DT

AP

AP/DT RAP

Type 1 terms 213
Type 2 terms 416

475
784

2.23
1.88

0.94
{

629 1259

2.00

{

Total

Type 1 terms: both the original form and its variants appear in the corpus.
Type 2 terms: only the variants appear in the corpus.
DT: number of di erent terms.
AP: total number of additional papers retrieved by variants, where only variants appear.
RAP: average of the ratio of the number of additional papers retrievedby variants to the number of papers retrieved by original terms.

Table 4: Additional papers retrieved by variants

X2 NS3 ! X2 S4 NS3 V S5 TP6]
< X2 cat>= N j NS < S4 lem>= `ga '< TP6 lem>= `reru ' j `rareru ']the performance of C will be improved by subdividing the category L (delimiter). Some delimiters aremainly used for punctuation between words, and others are mainly used for punctuation between clauses.
Therefore, we had better use only the former to dene
the meta-rule of type C shown in Table 2.
Let us now shift our observation to the additional
meta-rule X. The performance of X as a whole waslow (about 60% precision). However, among the three
types of X, the precision of X1 was 56.79%, that of
X2 was 91.41%, and that of X3 was 28.00%. As far
as simple performance is concerned, the precision of
X2 was close to that of proper meta-rules.
Most variations detected by the meta-rule X1 belong to the following two types.

Y2. Insertion of the case-making postpositional particle `wo ' between a sahen-noun and the sahenverb `suru '.

NS2 V S3 ! NS2 S4 V S3  < S4 lem>= `wo 'When there is no passive sux, insertion of the casemaking postpositional particle `ga' often changes therole of X2 S4 ] against NS3 V S5 ]. For instance, thecorrect variant of shisutemu teian (system proposal)
is not shisutemu ga teian suru] (in which shisutemu is
a subject of teian) but is shisutemu wo teian suru (inwhich shisutemu is an object of teian). This aects
the precision negatively.
As we previously mentioned, X2 produces high performance. The correct variants extracted through X2
were proved to be strictly dened by the following
meta-rules.

Y1. Insertion of the case-making postpositional particle `ga ' between nouns.
In this case, the second noun is a sahen-noun,
which is followed by the passive form of thesahen-verb `suru' (though the sahen-verb and thesux are not extracted).

Y3. Insertion or omission of TPNN between a noun
10

needed to determine whether we should add it to theproper rules or not.

and sux.

X2 X3 ! X2 TPNN4 X3  < X2 cat>=
N j NS < X3 cat>= TPNN j TPAN
X2 TPNN3 X4 ! X2 X4  < X2 cat>=
N j NS < X4 cat>= TPNN j TPAN

5

Discussion

In this paper, we have shown that the framework ofFastr can be successfully applied to Japanese term
variant detection, by which we can expect an improvement in recall without the sacrice of precision in IR,
if the framework is properly applied. However, different meta-rules give dierent performances. So, wemust consider the selection of meta-rules which satisfy
the demands of applications.
In the Japanese language, as we previously mentioned, there is no delimiting symbol which explicitly
divides words. Thus, we cannot clearly distinguishbetween syntactic variations and morpho-syntactic
ones. In this paper, by basically following the criteriaof the existing morphological analyser JUMAN 3.5,
we avoided the theoretical problem related to this.
Even practically, however, JUMAN 3.5 has problems
caused by inconsistencies. For instance, derivationalsuxes are sometimes treated as independent words
or morphemes7 . By adding a post-processing lter
to the results of JUMAN, we will be able to improvethe consistency of meta-rules.
There is another point which should be improved
concerning preparation of meta-rules. When Fastr extracts one variant, only one meta-rule is applied. In
other words, we cannot extract variants by the combination of meta-rules. In order to construct a practicalsystem, therefore, we have to describe a large numberof meta-rules which are made up of the combinationof the elemental variations explained in this paper.

This is a type of D (noun-noun variation), but theoriginal meta-rules that belong to D assumed thatthe sux be inserted between nouns and it could not
extract this type of variation.
X3 (permutation of nouns) gave the lowest precision. Most of the errors were caused by changes of therelationship between words which were resulted fromthe change of the word order. For instance, while
shisutemu shindan (system diagnosis) implies something which diagnoses a system, shindan shisutemu(diagnosing system) implies a system which diagnoses
something. However, when one of the nouns is asahen-noun, the relationships are kept identical inmost cases, though the head becomes the modier
and vice versa.

Y4. Permutation of nouns.

X2 NS3 ! NS3 X2  < X2 cat>= N j NS
NS2 X3 ! X3 NS2  < X3 cat>= N j NS

For instance, while memori kyoyu (memory sharing)is a process and kyoyu memori (shared memory) is a
result, the relation of the constituent elements is thesame, so in some cases the former may be regarded
as a correct variant of the latter.
The rule Y1, Y2 and Y3 might be described more
properly by type F (sahen-noun-verb), type B (compounding/decompounding) and type D (noun-noun
variation), respectively. As for Y4 (permutation ofnouns), further examination of other samples will be

For example, jiyuudo (degree of freedom) is divided into
two morphemes, jiyuu N] and do TPNN]. However, aimaido(degree of ambiguity) is recognised as one noun. In the latter
case, linking its derivatives is not possible.
7

11

minology." In Terminology and Knowledge Engineering '87, p. 123{136.

We are currently considering the possibility of building a meta-rule precompiler which can automaticallycompile a set of possible combinations of given metarules.
In addition to these, it is expected that it will bevery useful to exploit the semantic links of synonyms(cf. Jacquemin, 1999). In fact, as a descriptive framework, Fastr has the facility to treat semantic links as
well. By improving these aspects, it will be possible to construct a practically applicable robust term
variant recogniser for Japanese.

Koyama, T., Yoshioka, M. & Kageura, K.
(1998) \The construction of a lexically motivated corpus | The problem with dening lexical units." In
Proceedings of the First International Conference on
Language Resources and Evaluation, p. 1015{1019.
Kageura, K. (1994) \Dierences in the linguistic
representation of concepts in Japanese and English
complex noun terms." Terminology, 1(1), p. 103{119.

Matsumoto, Y., Kurohashi, S., Yamaji, O.,
Taeki, Y., and Nagao, M. (1998) Japanese Morphological Analysis System JUMAN Manual. ver. 3.5.
Kyoto: Kyoto University. In Japanese]
NACSIS, (1997) Introduction to the National Center
for Science Information Systems. Tokyo: NACSIS.
Nomura, M. (1978) \Setsujisei jion goki noseikaku." Densikeisanki ni yoru kokugo kenkyuu, IX,
p. 102{138.

References

EDR, (1996) EDR electronic dictionary version 1.5
technical guide. Tokyo: EDR.
Jacquemin, C. (1994) \Fastr: a unication-based
front-end to automatic indexing." In Proceedings, Intelligent Multimedia Information Retrieval Systems
and Management (RIAO'94), p. 34{47.
Jacquemin, C. (1996) \A symbolic and surgical acquisition of terms through variation." In: Wermter, S.
Rilo, E. & Scheler, G. (eds.) Connectionist, Statistical and Symbolic Approaches to Learning for Natural
Language Processing. Heidelberg: Springer. p. 425{
438.
Jacquemin, C. (1999) \Syntagmatic and paradigmatic representations of term variation." In Proceedings, 37th Annual Meeting of the Association for
Computational Linguistics (ACL'99), p. 341{348.

Jacquemin, C., Klavans, J. L. & Tzoukermann,
E. (1997) \Expansion of multi-word terms for index-

ing and retrieval using morphology and syntax." In
Proceedings, 35th Annual Meeting of the Association
for Computational Linguistics ((E)ACL'97), p. 24{
31.
Ishii, M. (1987) \Economy in Japanese scientic ter12