FUZZY WORD MEANING ANALYSIS AND REPRESENTATION IN LINGUISTIC SEMANTICS. 
AN EMPIRICAL APPROACH TO THE RECONSTRUCTION OF LEXICAL MEANINGS 
IN EAST- AND WEST-GERMAN NEWSPAPER TEXTS. 
Burghard B. Rieger 
Arbeitsgruppe f. mathem.-empirische Systemforschung (MESY) 
German Department, Technical University of Aachen, Germany 
Summary 
Word semantics is gaining increasing 
interest within linguistics in view of 
both, more adequate representational 
structures of the semantic system and 
methods and procedures to analyse it 
empirically. Due to the fact formal and 
operational means have been devised to 
describe and represent word connotation 
and/or denotation, this paper discusses 
some of the empirical problems connec- 
ted with natural languages' varying and 
vague meanings, how these can be ana- 
lysed statistically from discourse data, 
and represented formally as fuzzy system 
of vocabulary mappings. Some examples 
computed from East- and West-German 
newspaper texts will be given at the 
end to illustrate the approaches feasi- 
bility. 
I. Introduction 
When we look up linguistic theories 
of sentence- or even of text-semantics 
to see what they can offer in respect 
to word-meaning, we will be confronted 
with basically two types FILLMORE /3/ 
has referred to as checklist-semantics 
and prototype-semantics. According to 
this distinction, checklist-semantics 
provides listings of meaning components, 
semantic markers, or semantic descrip- 
tors which must be satisfied for a term 
to be (grammatically, truth-function- 
ally, or else) interpretable within a 
linguistic expression; whereas proto- 
type-semantics allows for the (pa~g- 
ma~l, Syn{~agmatical, or else) iden- 
tification of a term as part of a 
linguistic expression within a network 
structure of labeled nodes and relations. 
Examining how these listings and net- 
works are assembled, i.e. questioning 
from which sources and by what proce- 
dures the data necessary for their 
composition were acquired, we will 
invariably come accross the individual 
analysts', or group of analysts' own 
assumedly comprehensive and reliable 
knowledge of the world and/or the 
natural language system concerned. In 
the majority of cases, these will not 
have been made accessible by inter- 
subjectively defined operations but 
rather by way of intuitive introspec- 
tion. In doing so, linguists tend to 
make use of word-meaning instead of 
analysing it when they set up matrices 
for componential analysis or define 
semantic networks. Apart from tentative 
departures within generative semantics 
or statistical indexing, there have no 
operational procedures yet been devised 
for the semantic analysis and descrip- 
tion of natural language terms as a 
result of which - when applied to 
natural language discourse - a lexical 
structure may be obtained. 
Now, this is what word-semantics 
should and could do, and where exactly 
the problems begin. 
2. Epistomology 
If we agree that linguistics is, or 
at least ought to be, an empirical 
discipline, then the paradigm of empi- 
rical sciences should be followed, 
although it needs modification in view 
of the scope of natural language seman- 
tics. 
To adopt the paradigm of empirical 
sciences for linguistic research is 
tantamount to at least two postulates: 
a) not to rely on ready-made theories 
or models taken from another domain, 
because these may be inadequate in 
respect to the phenomena under in- 
vestigation; and 
76 
b) not to rely on the introspective 
exploration of one's own knowledge 
and competence as the allegedly in- 
exhaustible datasource although va- 
luable initial ideas might be prod- 
uced that way. 
Instead, the investigation of lin- 
guistic problems in general, and 
that of word -semantics in particu- 
lar, should start with hypotheses 
formulated for continuous estimation 
and/or testing against observable data, 
then proceed to incorporate the find- 
ings tentatively in some preliminary 
theoretical set up which finally may 
perhaps get formalized to become part 
of an encompassing theory. 
Within such a set up, the formal 
expressions which give an abstract re- 
presentation of the domain, and the 
numerical expressions which give a 
quantitative account of the ob- 
servable data, are normally to be 
complemented by correspondence rules. 
These allow for the operational 
interpretation of formal notations 
and theoretical constructs in terms 
of empirical methods of counting and 
measuring observable data. Linguistic 
theory has not been interested too 
much in developing correspondence 
rules of that kind so far /15/. 
Following the line of LABOV /9/ 
and LEECH /10/, prevailing linguis- 
tic theory and linguistic semantics 
in particular is dominated by what 
has been called the " categorial 
view " According to it, linguistic 
entities are at least implicitly 
asserted to be discrete, invariant, 
qualitatively distinct, conjunctively 
definable, and composed of atomic 
primes. Membership in categories, 
and relations of inclusion and ex- 
clusion among units and categories, 
are established by a deterministic 
type of rule that allows only for 
binary ( positive or negative ) or 
triple ( positive, negative, or op- 
tional ) assignment, but has no means 
to represent probable and/or possible 
degrees of transition. This type of 
rule - particularly when employed 
for meaning representation purposes 
- has come under severe criticism 
from as seemingly disparate dis- 
ciplines like cognitive theory 
( e.g. / 12 / ) and experimental 
psychology ( e.g. /20/ ) , information 
and computer science ( e.g. /23/, /4/), 
psycholinguistics ( e.g. /16/, /11/) , 
sociolinguistics ( e.g. /8/, /7/, 
computational semantics ( e.g. /14/), 
and artificial intelligence ( e.g. 
1221, 151) . 
From the increasing amount of strong 
empirical evidence piling, up in favour 
of some re-adjustment, a (meta-theore- 
tical) modification appears to be 
overdue. Accordingly, it may be argued 
that - contrary to the experimentally 
and simulatively well established 
(object-theoretical) fuzziness of 
cognitive categorizing and its linguis- 
tic correspondences - any formal re, 
presentation of it using only binary 
systems' notations will inevitably re- 
sult in inadequately sharp-edged 
lattices. When imposed upon the varying 
and vague structures constituted and 
modified continuously during the process 
of verbal communication observed to be 
modelled, this will render formal re- 
presentations of discrete entities with 
clear-cut boundaries where blurred 
margins and continuous transitions 
would be adequate. 
The modifications suggested so far 
may be summarized to concern both, the 
observable manifestation and/or formal 
representation of discourse, allowing 
gradual rather than abrupt transitions 
to account for imprecise phenomena in 
a precise way. This can be achieved, as 
I see it, formally by means of fuzzy set 
theoretical notations /24/, and opera- 
tionally by means of empirical proce- 
dures assigned to them /19/. Applied to 
natural language data, they will inter- 
relate observable but essentially fuzzy 
language phenomena on the one hand, and 
formal but finally categorial notations 
of their linguistic descriptions on 
the other. 
Thus, findings and/or hypotheses on 
either side may become testable against 
each other, allowing for mutual modifi- 
cations in the course of gradual im- 
provement and increasing adequacy of 
the model and what it represents. 
3. Structure of Meaning 
What makes the analysis of natural 
language meaning so intricate a problem 
depends on the particular nature of 
what has to be represented as its re- 
sults, namely, a representational 
structure in its own. It is this re- 
presentational aspect of language 
which theories of semantics and cogni- 
tion have been, and still are focussed 
on in particular. 
According to the more traditional 
theories, natural language meaning can 
be characterized by its denotative and 
connotative aspects. Denotation is 
understood to constitute referential 
meaning as a system of relations 
between words or sentences of a 
--77 
language and the objects or processes 
they refer to. Connotation is defined 
to constitute structural meanings as a 
system by which words or sentences of a 
language are conceptually re&ated to 
one another. Referential semantic the- 
ory is truth-functional and formally 
elaborated but as such not prepared to 
account satisfactorily for the vague- 
ness of natural language meaning; 
whereas structural semantics has con- 
sidered vagueness somewhat fundamental 
of language but, being based mainly 
upon intuitive introspection, it has 
not achieved the theoretical or me- 
thodological consistency of formal 
theories. 
In the course of recent, more pro- 
cedural approaches to cognition and 
language comprehension, the former dis- 
tinction of referential and structural 
meaning was embedded in what became to 
be known as frame semantics /17/. The 
central notion of it is that of memory 
which serve as a paradigm for the op- 
erational aspects of both, world sys- 
tem structures and language system 
structures. The basic distinction of 
what may propositionally be formulated 
as opposed to what may only prototypi- 
cally be realized in some system struc- 
ture of stored experiences, is reflec- 
ted in the great variety of notional 
pairings which different disciplines 
have produced facing a similar, if not 
identical research problem. Thus, their 
notions of formal vs. experient£al 
knowledge /2/, semantic vs. episodic 
memory /21/, frame vs. scene /3/, 
description vs. schema /I/, etc. show 
a striking resemblance: although their 
approaches differ in what they con- 
sider natural language meaning to be, 
they nontheless converge on the central 
notion of it, being a relation between 
a representation (i.e. the body of 
discourse) and that which it repre- 
sents (i.e. a referentially and/or 
prototypically defined system struc- 
ture). 
4. A formal approach 
It is this throughout relational 
structure of meaning that obviously 
allowed the concept of fuzzy sets and 
relations to be employed to incorporate 
vagueness into formal theories of se- 
mantics. 
The most recent, and at that most 
comprehensive approach (at least I 
know of) to tackle the problem of na- 
tural language meaning, is that of L.A. 
Zadeh /24/. Under the acronym PRUF for 
'Possibilistic, Relational, Universal, 
Fuzzy' he has devised a meaning repre- 
sentation language for natural languages 
which is possibilistic instead of truth- 
functional, and whose dictionary provides 
linguistically labelled fuzzy subsets of 
the universe, instead of sets of seman- 
tic markers under word-headings. 
The basic idea, upon which this 
approach hinges, is that a referential 
meaning may be explicated as a fuzzy 
correspondence between language terms 
and a universe of discourse. This corres- 
pondence, L, is formally defined to be a 
fuzzy binary relation from a set of 
language terms, T, to a universe of dis- 
course, U. As a fuzzy relation, L is 
characterized by a membership-function 
~L : T x U -~ ~,I\] ; x ~ T, z ~ U; 
O :~L(X,Z) ~ I (I) 
which associates with each ordered pair 
(x,z) its grade of membership ~L(X,Z) , 
being a numeric value between u ano I, 
in L, so that 
L ,: {((x,z), CL(x,z))} <2) 
The fuzzy relation L now induces a bilat- 
eral correspondence according to which 
a) the referencial meg ning of an element 
x' i~ T may be explicated as the 
fuzzy subset M(x') in U, assigned to 
it by the membership function ~L 
conditioned on x', 
M(x'):=,~L(Z,X'):= <((x',z 1),~n(x,zl)), 
.... ((X',Zn) , ~L(X',Zn)) > (3) 
b) the linguistic description of an 
element z' in U may be given as a 
fuzzy subset D(z') in T assigned to 
it by the membership function ~L 
conditioned on z' 
D(z'):=~(x,z'):: <((Xl,Z'),~L(Xl,Z'), 
• ..,((Xn,Z') , ~L(Xn,Z'))> (4) 
The definitions given in fuzzy sets 
theory for equality, containment, com- 
plement, intersection , and union allow 
for an application both, to referential 
meanings M(x) as subsets of elements in 
U and to linguistic descriptions D(z) 
as subsets of units in T. This corres- 
ponds to the distinction between scenic, 
or conceptual relations on the one hand, 
and frame, or semantic relations on the 
other - the latter of which only will be 
introduced here. 
Thus, synonymy of two terms x,x'~ T 
may be given as the equality of the two 
fuzzy subsets M(x) and M(x') repre- 
78 
senting the referential meaning in U 
X : X' iff ~L(Z,X)= ~L(Z,X') 
for all z (5) 
Partial synonymy may be defined by a 
similarity formula introducing some 
threshold-value s 
x~ x' iffl~L(Z,X) - ~L(Z,X') & s 
for all z (6) 
Hyponymy of a term x relative to x' may 
be explicated as containment of the 
meaning representing fuzzy sets con- 
cerned 
x~x' iff~L(z,x) ~(z,x') 
for all z (7) 
In so far as the operations of comple- 
ment, intersection and union are con- 
cerned which correspond to negation, 
conjunction and adjunction respective- 
ly, there has been some critical dis- 
cussion lately, particularly on the 
grounds of experimental results. These 
suggest that different definitions of 
operations should be maintained accord- 
ing to and comparable with the scene- 
frame-distinction aluded to above. For 
the generation of new meanings which 
denote possible but not yet labeled 
elements (or sets of elements) in U, it 
can well be argued that the following 
definitions should operate on both, 
referential meanings M(x) and linguis- 
tic descriptions D(z) the former of 
which only are given here. 
Negation (complement) : 
~x: = M(x) = I -~L(Z,X) for all z (8) 
Conjunctio ~ (intersection): 
x ~,x':= M(x~x')= 
minim L(z,x) ,~(z,x')\] for all z (9) 
Adjunction (union) : 
x vx': = M(xox')= 
max\[~L(Z,X\] ,~L(Z,X') \] for all z (10) 
Although formally satisfactory - as 
outlined and illustrated by PRUF - the 
approach's basic assumption concerning 
the referential nature of natural mean- 
ing proves to be crucial for its empir- 
ical applicability: in order to deter- 
mine the membership-grades of a fuzzy 
set, or fuzzy relation respectively, 
one has to have access to relevant 
empirical data defined to constitute 
the sets, and some operational means to 
calculate the numerical values from 
these data. 
As the domain of the fuzzy relation 
~ contain not only the set of terms iL 
of a language, T, but also the set of 
objects and/or processes these terms are 
believed to denote in the universe, U, 
both these sets should be accessible in 
order to let an empirical procedure be 
devised that could be assigned to ;~T." 
All that Zadeh /24/ is offering in That 
respect, stays empirically rather vague. 
He assumes that "each of the symbols or 
names in T may be defined ostensively 
or by exemplification. That is by 
pointing or otherwise focussing on a 
real or abstract object in U and indi- 
cating the degree - on the scale from 
0 to I - to which it is compatible with 
the symbol in question". 
This cannot be considered a solution 
which may be called both adequate and 
operational in the above sense. Taken 
to be executable, Zadeh's suggestion 
necessarily involves probands' question- 
ing about what they think or believe a 
term denotes. Thus, the procedure would 
again have to rely on the individual in- 
trospection of a multitude of competent 
speakers, instead of making these 
speakers employ the term's denotational 
and/or connotational function in the 
course of communicative verbal inter- 
action. However, experimental psychol- 
ogy has taught us to expect considerate 
differences between what people think 
they would do under certain presupposed 
conditions, and what in fact they will 
do when these conditions are real. And 
there is every reason to assume that 
this difference is found in cases of 
language performance, too. 
So, it would appear more appropriate 
to make natural language use the basis 
for identifying those language regular- 
ities, which under certain communica- 
tion frame conditions real speakers/ 
hearers follow and/or establish in dis- 
course. These will consequently allow 
natural language meaning (whatever that 
may be) not only to be intended and 
understood, but also to be analysed and 
represented. As this apparently is the 
only certainty about meaning anyway, 
namely that it can only be constituted 
by means of natural language texts, 
these should also be able to provide 
the necessary data with the advantage 
of being empirically accessible. As- 
sembled in a pragmatically homogeneous 
corpus, the usage regularities which 
the lexical items produce, may thus be 
79 
analysed statistically with the numer- 
ical values obtained to define fuzzy 
vocabulary mappings /16/. 
5. An empirical reconstruct!on 
Following this line of argument is 
to ask for a connotational supplement 
to the denotational approach Zadeh for- 
warded so far. This goes along with a 
necessary re-interpretation of what the 
sets T and U (I) in the referential 
meaning relation possibly stand for. 
From a structural point-of-view, T is 
not just a set of terms of a language 
any more, but a system of lexical units 
the usage regularities of which induce 
a relational structure of its own. So, 
this structure does not just allow for 
a set of objects and/or processes in U 
to be denoted, but it constitutes them 
as a system of concept-points, which is 
dependent on, but not identical with 
the one induced by the usage regular- 
ities of terms as employed and identi- 
fied in natural language discourse /17/. 
Thus, being a non-symmetric, fuzzy, 
binary relation, ~ can empirically be 
reconstructed only on the basis of 
natural language discourse data. So 
far, statistical procedures have been 
used for the reconstruction by a con- 
secutive mapping in three stages from 
T to U, providing the membership-grades 
f°ro~nLthe" first stage co-occurrences of 
terms are not just counted but the in- 
tensities of co-occurring terms in the 
texts of the database are calculated. 
This is done by a modified correlation- 
coefficient ~ that measures mutual (po- 
sitive) affinity or (negative) repug- 
nancy of pairs of terms x,x'~T by real 
numbers from the interval \[-I, +I\]. 
can therefore be considered a fuzzy re- 
lation in the Cartesian-product of the 
set of terms T used in the texts ana- 
lysed 
: T x T,~,, I : \[-I,+I\] ; 
:: {xi  , i:I ..... n T (11 
By conditioning this fuzzy relation 
on the x~T, we get a non-fuzzy mapping 
~Ixi : T ---> I n, C := I n (12) 
This mapping assigns to each x~T one 
and only one so-called corpus-point y 
defined by the n-tupel of membership- 
grades~(xi,x) in the corpus space C 
~(xi,x) := y~C (13) 
Each corpus-point y'~ C may thus be 
considered a formal notation of the 
usage regularities, measured by grades 
of intensity, any one term x' shows 
against all the other terms x.~/ T. 
On the second s.tage the di{ferences 
of usage are calculated. This is done by 
a distance measure ~ , which yields real, 
non-negative, numeri£al values from an i 
interval standardized to \[0,1\] to denote 
the distances between any two corpus- 
points y,y'~ C. ~I can also be consi- 
dered a fuzzy, binary relation in the 
set of all corpus-points y. defined to 
constitute the corpus space l C 
~I : C x C --~ I; I := \[O,1\]; 
C :: \[yi} , i = I ..... n (I 4) 
By conditioning this fuzzy relation ~. 
on the Yi (or - following (13) - the ~ 
x. respectively) we get a non-fuzzy i 
mapping 
$11xi : C--tin; U := I n (15) 
This mapping assigns to each y~C (or 
x6T respectively) one and only one so- 
called meaning- or concept-point z de- 
fined by the n-tupe! of distance-values 
in the semantic space U, 
~I (Yi 'x) : ~I (Yi 'y) :: zeU (16) 
Each concept-point z'~ U may thus be 
considered a formal notation of all the 
differences of all usage regularities, 
as a function of which the meaning of a 
term x'~ T can be characterized. 
Therefore it can be identified - 
according to (13) - with (4) , i.e. the 
l iniguistic d@iScrliptlion , D (z') , of a 
concept-point z' which is a fuzzy sub- 
set in T 
~1(xi,z') := D(z') C T (17) 
On the third stage of the conse- 
cutive mapping, there will topological 
environments of concept-points be cal- 
culated - in analogy to (14) - by a 
distance measure ~9 which specifies the 
distances between ~ny two z,z'~ U. Thus 
again, ~92 may also be interpreted as a 
fuzzy, binary relation in the set of 
all concept-points z defined to con- 
stitute the semanticlspace U 
~2 : U x U--~ I ; I := \[O,1\]; 
U ::{Zi}; i:I .... ,n (18) 
The conditioning of ~^ on the z re- 
sults in a non-fuzzy ~apping i 
80 
~ ~02\[z i : U-->I n (19) 
which assigns to each z~U (and - fol- 
lowing (16) - x~T respectively) one 
and only one n-tupel of distances that 
- scaled according to decreasing values- 
will constitute the environment E(z) 
$2(zi,x) = $2(zi,z):= E(z) (20) 
Any such environment E(z') can be con- 
sidered a formal means to describe the 
position of a concept point z' by its 
adjacent neighbours in the semantic 
space which is constituted by functions 
of differences of language usage regu- 
larities. E(z') can therefore be iden- 
tified - following (16) and (20) - with 
(3) the conceptual meaning, M(x'), of a 
term x' which is a fuzzy subset in U 
~2(zi,x') := M(x') c U (21) 
We are now in the position to assign 
to the fuzzy relation 
: T x (22) 
and the two-sided correspondence 3) 
and (4) induced by it, the following 
operations. 
The two distance measures ~ (14) and 
~9 (18), operating an numerical" data 
obtained from the correlational analy- 
sis (11) of lexical items employed in a 
corpus of natural language texts, will 
determine the membership-grades to be 
associated with (22), namely for the 
correspondence (4) induced by tL accord- 
ing to (15) inserting 
al)Xi:= , (xi,z i) = T (23) 
and for its inversion the correspond- 
ence (3) according to (19) inserting 
%Izi (24) 
This concludes the empirical recon- 
struction, leaving open only the coeffi- 
cients alluded to above. 
Given the lemmatized vocabulary V as 
a proper subset of T of lexical units 
V :={xi~ ; i=1,...,n 
employed in a corpus K of natural lan- 
guage texts as specified above 
K :=~t} ; t=1,...,m 
where 
m 
~- st:= S S = st; I t 
t=1 
(25) 
is the sum S of all text-lengths s t 
measured by the number of lexical 
units (tokens) in the corpus, and 
m 
H = ~ ht! I t ~ h t ~ H 
t=1 
(26) 
is the total frequency H of a lexical 
unit x (type) computed over all texts 
in the corpus, then the modified corre- 
lation-coefficient ~x to be inserted 
into (11) reads 
m 
~_(h t- ht*)'(h \[- ht~) 
O4(X,X') = t=1 
htrht~ ) 2 (h\[_h\[~)2 /2 
\t=l t=l 
-I ~.~(X,X') ~ +I (27) 
with 
~= H ,~= H I h t \[ s t and h t \[ s t (28) 
The distances have been calculated 
according to the following measures 
which for c91 (14) reads 
n 2.. 
I ..... 
i=i,~( x,xi) 2 +~t(x', X i)2 ; 
O-Z ~I (Y'Y') L 2 (29) 
and ~2 (18) reads 
~2(z,z )(in=~1(~1(Y,Yi ) 21(Y ,Yi))2) I/2 I = -- I ; 
(3o) 
-81 
As these distance measures satisfying 
the conditions are to be considered the 
metric of the corpus space C and the se- 
mantic space U respectively, it should 
be noted here that so far the assumption 
of it being Euclidean (30) is nothing but 
a fist (although operational) guess. Ex- 
periments with different distance meas- 
ures one of which is (29) are currently 
undertaken. Eventually, these might 
prove to be more adequate one day in mod- 
elling word-semantic systems'structures. 
CONCEPTUAL MEANING M(X) DIE WELT 
X = EUROPA/ISCH 
VERT~AG ,46g GEM\[IMSCIIAFT/CH ,507 
VOLK ,540 GEWnHNHEIT/LICll ,541 
HINISTER/IUM .546 DEUTScH/tAND ,548 
TEILNAHME/N ,554 STAAT .55R 
KRIER/ERIScH ,578 RLUT/EN ,585 
AMERIKA/ER/ISCH .5~7 BESIlCH/EN ,588 
ERI<I.AEREN/UNG ,589 HOL|AHD/ER/ISCH .594 
FRAKTION .594 HOFrEN/UNA .595 
REGIEREN/UNG ,598 SOWJET/15CH/ION ,598 
PRAESIDENT .615 WELT .625 
FRIEDE/LICIt .6~9 DELEGATION/ER .62g 
ANTWORT/EN ,631 BLIND ,633 
KOHMUNIST/MUS ,634 ATLAHTiK/SCH ,635 
NATION/AL .636 CI~INA/SIgCH ,639 
HACIIT/IG .639 VERSUcH/FN ,641 
CONCEPTUAL MEANING M(X) NEUES DEUTSCHLAND 
X = EUROPA/ISCH 
SPALT/EN ,136 ANTI ,165 
MILITARIST/MUS ,197 VERSUCH/EN ,204 
FRANKREIcH/ISCR ,~04 HERR/EN/ScHAFT ,256 
ABSICHT ,261 REVANCHE/IST ,279 
BEWEOEN/UNG ,309 LIEBE/N ,326 
SCNWER ,361 STREHEN/UNG ,354 
OSTEN ,373 DOKTRIN ,381 
SAGEN ,392 SpORT/LER ,392 
WERK/E R ,402 WATT ,402 
DYNAMO ,402 WALZE ,602 
BLECN ,402 HOEREN ,408 
FUEHRUNG/EN ,416 MINISTER ,427 
ABGEORDNETER ,432 RAD ,434 
NATION/AL ,438 FAHREN/ER/T ,442 
HALBJAHR ,667 GANZ ,652 
6. Examples 
To show the feasibility of the em- 
prirical approach and to leave you not 
completely empty-handed at the end, the 
following examples of linguistic des- 
cription D(z) and of conceptual mean- 
ings M(x) may serve as an illustration. 
They are taken from the data of a 
pilot-study on semantic differences in 
lexical structure /18/ that has been 
done within a major project on East- 
LINGUISTIC DESCRIPTION D(Z) DIE WELT 
Z = EUROPA/ISCH 
VERT~AG ~,233 TEILNAHME/N I.355 
8ESIICIt/EN 1.303 GFWOHNHEIT/LICH 1,422 
KRIEG/ERIScH 1.557 ~ACHSEN/TI~N 1,56S 
VERSIICH/EN \] ,503 NACIIT/I O i .5q2 
AHERIKA/ER/ISCH 1.66\] VOLK 1.680 
NATI~N/AL 1.6~5 PROTnKOII I,715 
ATLANTIK/SCIi 1,726 OSTEN 1,743 
DEIITSCHILAND 1,74~ DELFGATION/ER 1.746 
SCHWI~RIn/KEIT 1.747 ANTWORT/EH 1.7bO 
ATOHIAR 1.772 KnMMLINIST/MIJ~ i,S47 
ENTSCIIEIDEN/UNG 1.857 APPELL/TERFN 1,877 
KAHPr/EH 1.910 STARK/E 1,946 
REPUBLIK/ANISCH 1.949 CHINA/ST~CH 1,95~ 
POLITIK/ER/ISCH 1.952 NINISTER/IHM 1.954 
FRIEDE/LICH i,g83 ARKOHHEM 1,97~ 
LINGUISTIC DESCRIPTION D(Z) NEUES DEUTSCHLAND 
Z = EUROPA/ISCH 
SPALT/EN ,955 ANTI 1.049 
MILITARIST/MUS 1.blO REVANCHE/IST 2.026 
FUEHRUNG/EN 2,U49 SAOEN 2,148 
NATION/AL ~,I75 STREHEN/UNG 2,~96 
GA('IZ ~.6~9 SCHWEH 2.646 
HERR/EH/SCNAFT 2.718 8EWEGEN/UNG 2.753 
OSTEN ~.770 VERSUCH/EN 2.~18 
FRANKREICH/ISCH 2°~18 LIERE/N 2,H42 
KOENNEN 2,Hb3 MINISTER 2.893 
HAND 2,953 GEHEN 3.0|8 
HOEREN 3,077 GAHE/EN 3.a04 
ELEKTRO/NISCH 3,Coo ARSIcHT 3.358 
HAUPT 3°~38 TRITT/EN 3,441 
LANG 3,466 POLITIK/ER/ISCH 3.530 
HACHEN 3.541 OEKONOMIScH ~.SQR 
Table I 
Conceptual Meaning M(x) and Linguistic Description D(z) of EUROPA/ISCH as employed in 
the newspapers DIE WELT and NEUES DEUTSCHLAND, calculated according to (29) and (30) . 
CONCEPTUAL MEANING M(XAX' 
X = SKI, X' = ABFAHRT/EN 
SKI ,~68 
LIFT .305 
TOUR ,371 
AUTO .431 
BERG .441 
SPORT/LER ,456 
BAHN ,55H 
DOPPEL .573 
STRASZE .639 
STUECK ,848 
ORT ,672 
JAZZ .686 
KUR ,720 
STUNDE ,72T 
ARZT/LICH ,731 
DIE WELT LINGUISTIC DESCRIPTION D(Z) DIE WELT 
Z = XAX',,X = SKI, X' = ABFAHRT/EN 
ABFAHRT/EN ,Z68 SKI .598 ABFAHRT/EN ,598 
PISTE ,370 BERG 1,189 URLALIB 1,259 
ALPPN ,385 LIFT 1,303 TOUR 1,35B 
FAHREN/T/E~ .437 SPOPT/LER 1.427 PISTE Io435 
URLAUB .442 RETTFM/UNG 1,497 ALPEN 1,606 
RETTEN/UNG ,525 AUTO 1.891 BAHN 2,011 
LAUF/EN ,568 FAII~EN/T/ER 2.089 ORT 2,103 
LUFT ,620 STUFCK 2,104 LAUT/EN 2,131 
GAST ,648 LUFT 2.321 DOPPEL 2,415 
LAUT/EN ,667 MARKT 2,526 STANN/EN 2,562 
MUSIK/ALIScH/P .682 TOD 2,571 .HFPZ 2,603 
TOo ,719 HOLLAND/ER/ISCH 2°608 MUSiK/ALIScH/P 2.610 
MARKT ,727 PRFTS 2o641 UEBERZEUGEN/UNG 2.642 
RAD/N ,729 JAZZ 2.653 ALT/ER 2.65R 
HOLLAND/ER/IScH ,732 ALtE 2,660 STRASZE 2,663 
Table 2 
Conjunction of the Conceptual Meanings of SKI and of ABFAHRT/EN, M(xAx') , and the 
resulting concept point's Linguistic Description D(zlz = x.~x') . 
82 
West-German language comparison. 
So far, two samples from corpora 
consisting of texts from the East-Ger- 
man newspaper 'Neues Deutschland' and 
the West-German newspaper 'Die Welt' 
have been analysed according to the 
procedures outlined. Although the sam- 
ples analysed are rather small - approx- 
imately 3000 running words (tokens) of 
roughly 300 lemmatized words (types) - 
the results look quite promising to the 
native speaker of German. In mapping the 
connotational difference which some 
morphologically identical German lexi- 
cal entries have developed almost si- 
multaneously after twenty years of us- 
age in a derided country's rather 
strictly separated population, the 
pilot-study's results seem to indicate 
that - linguistically - an additional 
analysis of comparable text-corpora of 
earlier and/or later years could pro- 
vide the diachronic complement to the 
so far synchr0nic investigation into 
the lexical structures concerned, al- 
lowing for the empirical reconstruction 
not only of their instantaneous word- 
meanings, but of their time-dependent 
procdural changes that Nowakowska /13/ 
aims at. Being induced by varying lan- 
guage usages, these can operationally 
be analysed as regularities followed 
and/or established by language users to 
differing degrees, which hence may 
formally be represented as functions 
that constitute dynamic systems to mod- 
el semiotic structures. 
In the above Tables I and 2 the 
linguistic depcription D(z) of a con- 
cept point z is given as well as the 
conceptual meg ning M(x) of a vocabulary 
term x from both of the newspaper corp- 
ora further details of which may be 
found in /18/. 
Acknowledgement 
This paper an earlier version of 
which was presented under the title 
"Fuzzy Representation Systems in Lin- 
guistic Semantics" at the 8th European 
Meeting on Cybernetics and Systems Re- 
search (EMCSR/8) in Vienna, Austria, in 
April 1980, is in some parts identical 
with /19/. It takes up the model con- 
struction resulting from a project in 
Empirical Semantics supported by the 
Northrhine-Westphalia Ministry of 
Science and Research, applied to the 
language data provided by the German 
Research Foundation's project on East- 
West-German language comparison. I would 
like to thank Dr. H.M. Dannhauer for 
providing his programming abilities to 
process these language data so effi- 
ciently at the Technical University of 
Aachen Computing Centre. 

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