SEMANTIC RELEVANCE AND ASPECT DEPENDENCY IN A GIVEN SUBJECT DOMAIN 
Contents-drlven algorithmic processing of fuzzy wordmeanings 
to form dynamic stereotype representations 
Burghard B. Rieger 
Arbeitsgruppe fur mathematisch-empirische Systemforschung (MESY) 
German Department, Technical University of Aachen, 
Aachen, West Germany 
ABSTRACT 
Cognitive principles underlying the (re-)construc- 
tion of word meaning and/or world knowledge struc- 
tures are poorly understood yet. In a rather sharp 
departure from more orthodox lines of introspective 
acquisition of structural data on meaning and know- 
ledge representation in cognitive science, an empi- 
rical approach is explored that analyses natural 
language data statistically, represents its numeri- 
cal findings fuzzy-set theoretically, and inter- 
pret5 its intermediate constructs (stereotype mean- 
ing points) topologically as elements of semantic 
space. As connotative meaning representations, 
these elements allow an aspect-controlled, con- 
tents-driven algorithm to operate which reorganizes 
them dynamically in dispositional dependency struc- 
tures (DDS-trees) which constitute a procedurally 
defined meaning representation format. 
O. Introduction 
Modelling system structures of word meanings and/or 
world knowledge is to face the problem of their 
mutual and complex relatedness. As the cognitive 
principles underlying these structures are poorly 
understood yet, the work of psychologists, AI-re- 
searchers, and linguists active in that field ap- 
pears to be determined by the respective disci- 
pllne's general line of approach rather than by 
consequences drawn from these approaches' intersec- 
ting results in their common field of interest. In 
linguistic semantics, cognitive psychology, and 
knowledge representation most of the necessary data 
concerning lexical, semantic and/or external world 
information is still provided introspectively. Be- 
searchers are exploring (or make test-persons ex- 
plore) their own linguistic/cognitive capacities 
and memory structures to depict their findings (or 
let hypotheses about them be tested) in various 
representational formats (lists. arrays, trees, 
nets, active networks, etc.). It is widely accepted 
that these modelstructures do have a more or less 
ad hoc character and tend to be confined to their 
limited theoretical or operational performances 
within a specified approach, subject domain or im- 
plemented system. Basically interpretative approa- 
ches like these, however, lack the most salient 
characteristics of more constructive modelstruc- 
tures that can be developed along the lines of an 
entity-re!stlonshio approach (CHEN 1980). Their 
properties of flexibility and dynamics are needed 
for automatic meaning representation from input 
texts to build up and/or modify the realm and scope 
of their own knowledge, however baseline and vague 
that may appear compared to human understanding. 
In a rather sharp departure from those more ortho- 
dox lines of introspective data acquisition in mea- 
ning and knowledge representation research, the 
present approach (I) has been based on the algo- 
rithmic analysis of discourse that real speakers/ 
writers produce in actual situations of performed 
or intended communication on a certain subject do- 
main, and (2) the approach makes essential use of 
the word-usage/entity-relationship paradigm in com- 
bination with procedural means to map fuzzy word 
meanings and their connotative interrelations in a 
format of stereotypes. Their dynamic dependencies 
(3) constitute semantic dispositions that render 
only those conceptual interrelations accessible to 
automatic processing which can - under differing 
aspects differently - be considered relevant. Such 
dispositional dependency structures (DDS) would 
seem to be an operational prerequisite to and a 
promising candidate for the simulation of contents- 
driven (analogically-associative), instead of for- 
mal (logically-deductive) inferences in semantic 
processing. 
I. The approach 
The empirical analysis of discourse and the formal 
representation of vague word meanings in natural 
language texts as a system of interrelated concepts 
(RIEGER 1980) is based on a WITTGENSTEINian assump- 
tion according to which a great number of texts 
analysed for any of the employed terms' usage regu- 
larztie~ will reveal essential parts of the con- 
cepts and hence the meanings conveyed. 
It has been shown elsewhere (RIEGER 1980), that in 
a sufficiently large sample of pragmatically homo- 
geneous texts,called corpus, only a restricted vo- 
cabulary, i.e. a limited number of lexical items 
will be used by the interlocutors however compre- 
hensive their personal vocabularies in general 
might be. Consequently, the lexical items employed 
to convey information on a certain subject domain 
under consideration in the discourse concerned will 
be distributed according to their conventionalized 
communicative properties, constituting semantic re- 
gu!aritiez which may be detected empirically from 
the texts. 
For the quantitative analysis not of propositional 
strings but of their elements, namely words in na- 
tural language texts, rather simple statistics ser- 
ve the basicalkly descriptive purpose. Developed 
from and centred around a correlational measure to 
specify intensities of co-occurring lexical items 
used in natural language discourse, these analysing 
298 
algorithms allow for the systematic modelling of a 
fragment of the lexical structure constituted by 
the vocabulary employed in the texts as part of the 
concomitantly conveyed world knowledge. 
A correlation coefficient appropriately modified 
for the purpose has been used as a mapping function 
(RIEGER 1981a). It allows to compute the relational 
interdependency of any two lexical items from their 
textual frequencies. Those items which co-occur 
frequently in a number of texts will positively be 
correlated and hence called affined, those of which 
only one (and not the other) frequently occurs in a 
number of texts will negatively be correlated and 
hence called repugnant. Different degrees of word- 
repugnancy and word-affinity may thus be ascer- 
tained without recurring to an investigator's or 
his test-persons' word and/or world knowledge (se- 
mantic competence), but can instead solely be based 
upon the usage regularities of lexical items obser- 
ved in a corpus of pragmatically homogeneous texts, 
spoken or written by real speakers~hearers in ac- 
tual or intended acts of communication (communica- 
tive performance). 
2. The semantic space structure 
Following a system-theoretic approach and taking 
each word employed as a potential descriptor to 
characterize any other word's virtual meaning, the 
modified correlation coefficient can be used to map 
each lexical item into fuzzy subsets (ZADEH 1981) 
of the vocabulary according to its numerically spe- 
cified usage regularities. Measuring the differen- 
ces of any one's lexical item's usages, represented 
as fuzzy subsets of the vocabulary, against those 
of all others allows for a consecutive mapping of 
items onto another abstract entity of the theoreti- 
cal construct. These new operationally defined en- 
tities - called an item's meanings - may verbally 
be characterized as a function of all the diffe- 
rences of all regularities any one item is used 
with compared to any other item in the same corpus 
of discourse. 
UNTERNEHM/enterpr 0.000 
SYSTEM/system 2.035 
ELEKTR/electron 2.195 
DIPCOM/diploma 2.288 
INDUSTR/industry 2.538 
SUCHE/search 2.772 
SCHUC/school 2.922 
FOLGE/consequ 3.135 
ERFAHR/experienc 3.485 
ORGANISAT/organis 3.84b 
GEBIET/area 4.055 
LEIT/guide 2.113 
COMPUTER 2.208 
VERBAND/assoc 2.299 
STELLE/position 2.620 
SCHREIB/write 2.791 
AUFTRAG/order 3.058 
BERUF/professn 3.477 
UNTERR/instruct 3.586 
VERWALT/administ 3.952 
WUNSCH/wish/desir 4.081 
,o. 
Table I: Topological environment E<UNTERNEHM> 
The resulting system of sets of fuzzy subsets con- 
stitutes the semantic space. As a distance-relatio- 
nal datastructure of stereotypically formatted mea- 
ning representations it may be interpreted topo- 
logically as a hyperspace with a natural metric. 
Its linguistically labelled elements represent mea- 
ning points, and their mutual distances represent 
meaning differences. 
The position of a meaning point may be described by 
its semantic environment. Tab.1 shows the topologi- 
cal envlronment E<UNTNEHM>, i.e. those adjacent 
points being situated within the hypersphere of a 
certain diameter around its center meaning point 
UNTERNEHM/enterprise as computed from a corpus of 
German newspaper texts comprising some 8000 tokens 
of 360 types in 175 texts from the 1964 editions of 
the daily DIE WELT. 
Having checked a great number of environments, %t 
was ascertained that they do in fact assemble mea- 
ning points of a certain semantic affinity. Further 
investigation revealed (RIEGER 1983) that there are 
regions of higher point density in the semantic 
space, forming clouds and clusters. These were de- 
tected by multivariate and cluster-analyzing me- 
thods which showed, however, that the both, para- 
digmatically and syntagmatically, related items 
formed what may be named connotatlve clouds rather 
than what is known to be called semantic fle!ds. 
Although its internal relations appeared to be un- 
specifiable in terms of any logically deductive or 
concept hierarchical system, their elements' posi- 
tions showed high degree of stable structures which 
suggested a regular form of contents-dependant as- 
sociative connectedness (RIEGER 19Bib). 
3. The dispositional dependency 
Following a more semiotic understanding of meaning 
constitution, the present semantic space model may 
become part of a word meaning/world knowledge re- 
presentation system which separates the format of a 
basic (stereotype) meaning representation from its 
latent (dependency) relational organization. Where- 
as the former is a rather static, topologically 
structured (associative) memory representing the 
data that text analysing algorithms provide, the 
latter can be characterized as a collection of dy- 
namic and flexible structuring processes to re- 
organize these data under various principles (RIE- 
6ER 1981b). Other than declarative knowledge that 
can be represented in pre-defined semantic network 
structures, meaning relations of lexical relevance 
and semantic dispositlons which are haevlly depen- 
dent on context and domain of knowledge concerned 
will more adequately be defined procedurally, i.e. 
by generative algorithms that induce them on chang- 
ing data only and whenever necessary. This is 
achieved by a recursively defined procedure that 
produces hierarchies of meaning points, structured 
under given aspects according to and in dependence 
of their meanings' relevancy (RIEGER 1984b). 
Corroborating ideas expressed within the theories 
spreading activation and the process of priming 
studied in cognitive psychology (LORCH 1982), a new 
algorithm has been developed which operates on the 
semantic space data and generates - other than in 
RIEGER (1982) - dispositional dependency structures 
(DDS) in the format of n-ary trees. Given one mean- 
ing point's position as a start, the algorithm of 
least distances (LD) w~ll first list all its neigh- 
bouring points and stack them by increasing distan- 
ces, second prime the starting point as head node 
or root of the DDS-tree to be generated before, 
third, the algorithm's generic procedure takes 
over. It will take the first entry from the stack, 
generate a list of its neighbours, determine from 
it the least distant one that has already been 
primed, and identify it as the ancestor-node to 
299 
whlcn the new point is linked as descendant-node to 
be primed next. Repeated succesively for each of 
the meaning polnts stacked and in turn primed in 
accordance with this procedure, the algorithm will 
select a particular fragment of the relational 
structure e latentlv inherent in the semantic space 
data and depending on the aspect, i.e. the initial- 
ly primed meaning point the algorithm is started 
with. Working its way through and consuming all 
lapeled points in the space structure - unless 
stopped under conditions of given target nodes, 
number of nodes to be processed, or threshold of 
maximum distance - the algorithm transforms pre- 
vailing similarities of meanings as represented by 
adjacent points to establish a binary, non-symme- 
tric, and transitive relation of semantic relevance 
between them. This relation allows for the hierar- 
chical re-organization of meaning points as nodes 
under a pr,med head in an n-arv DDS-tree (RIEGER 
1984a). 
Without introducing the algorithms formally, some 
of their operatlve characteristics can well be il- 
lustrated in the sequel by a few simplified examp- 
les. Beginning with the schema of a distance-like 
data structure as shown in the two-dimensional con- 
figuration of 11 points, labeled a to k (Fig. I.I} 
the stimulation of e.g. points a or c will start 
the procedure and produce two specific selections 
of distances activated among these 11 points (Fig. 
1.2). The order of how these particular distances 
are selected can be represented either by step- 
lists (Fig. 1.3), or n-ary tree-structures (Fig. 
1.41, or their binary transformations {Fig. 1.5). 
It is apparent that stimulation of other points 
within the same configuration of basic data points 
will result in similar but nevertheless differing 
trees, depending on the aspect under which the 
structure is accessed, i.e. the point initlally 
stimulated to start the algorithm wlth. 
Applied to the semantic space data of 360 defined 
meaning points calculated from the textcorpus of 
the t964 editions of the German newspaper DIE WELT, 
the Dispositional Dependency Structure ¢DDS) of 
UNTERNEHMlenterprise is given in Fig. 2 as gene- 
rated by the procedure described. 
Beside giving distances between nodes in the DDS- 
tree, a numerlcal measure has been devised which 
describes any node's degree of relevance according 
to that tree structure. As a numerical measure, a 
node's crzteriality is to be calculated with re- 
spect to its root or aspect and has been defined as 
a function of both, its distance values and its 
level tn the tree concerned. For a w~de range of 
purposes ~n processing DDS-trees, different crlte- 
rialities of nodes can be used to estimate which 
paths are more likely being taken against others 
being followed less likely under priming of certain 
meanlng points. Source-orlented, contents-drlven 
search and rattlers! procedures may thus be perfor- 
med effectively on the semantlc space structure, 
allowing for the actlvatlon of depeneency paths. 
These are to trace those intermediate nodes which 
determine the associative transitions of any target 
node under any specifiable aspect. 
f 
e 
d h 
J 
Fig. I.I 
£ 
d b d.c. 
l 
Step Zd Za 
0 a -÷ a 
1 e -@ a 
2 b -@ a 
3 c -÷ b 
4 f -@ e 
5 g -9 a 
6 d -~ b 
7 h -÷ g 
8 i -~ h 
9 k -÷ b 
I0 J -÷ c 
Fig. 1.2 
Ste Zd Za 
0 c -~ c 
I j -~ c 
2 i -÷ c 
3 b -~ c 
4 h -} i 
5 k -~ b 
6 a -} b 
T 9 -÷ h 
8 d -÷ b 
9 e -~ a 
!0 f -÷ e 
I /l\ I 
f c d k h 
I 
J i 
Fig. 1.3 
h k a d 
I I 
r 
f 
8 
v 
e 
f v 
f c 
I 
Fig. 1.4 
c 
v v v 
d k n h 
I 1 
1 g 
Fig. 1.5 
¥ 
b 
v v 
k ,m 
J 
m 
I 
f 
300 
AHT 5.326/.158 
FOLGE 3.135/.242 
UNTERNEHMEN ~. SYSTEM 
O.OOO/1 .00 2.035/ .329 
==.VERNANDELN 
4.559JO50 
BERUF ==ERFAHREN 
2.521/.115 2.677/.O41 
~. GEUIET 
==INDOSTRIE 
1,104/.230 
F~HIG r 1.86o/.o22 
~¢~ORGANISA'I' 1.88B/.o21 
UOCH ~ 4.O23/.O15 
M~.GCH INE 
3.310/.O1~ 
HERRSCHAFT L 3.445/.O63 ~3.913/.O16 
STELLE KOSTEN 
2 .OO3/. IO3 > 4 .644/.022 
=AUFTRAG 
1.923/.089 
=,SUCHE 
O.720/.207 
:~VERBAND O.734/.204 
• TECIINIK ~1.440/.O15 
==AUSGA~E 2.220/.009 
BKITE ~a.531/.005 
~ 1.227/.012 2.165/.LOb KENNEN EiNSATZ RADM 
\].513/.O10 ~='4.459/.OO2 ~='3,890/.iX~I 
WIRT~CI~FT F 
3.459/.O11 
VERWALTEN VEHANTWORTK ENTWZCKELN 2.650/.O90 =>'2.242/.O39 N1~"3.405/.Oll 
UNTERRICHT 1.583/.142 
SCllULE NUNI:iCli 1.150/.186 ;~"1.795/.O94 
I 
t 
SCHREIUEN 1.257/.173 
LEITEN LOEL~:KTRO COMPUI'Ek =" 1.425/. 188 .528/,263 O.O95/,735 
Fi 
Using these tracing capabilities wthin DDS-trees 
proved particularly promising in an analogical, 
contents-driven form of automatic inferencing 
,hich - as opposed to logical deduction - has ope- 
rationally be described in RIEGER (1984c) and simu- 
lated by pay of parallel processing of two (or 
more) dependency-trees. 
REFERENCES 
Chen, P.P.(1980)(Ed.): Proceedings of the Ist In- 
tern. Conference on Entity-Relationship Ap- 
proach to Systems Analysis and Design (UCLA), 
Amsterdam/NewYork (North Holland) 1980 
Lurch, R.F.(1982): Priming and Search Processes in 
Semantic Memory: A Test of Three Models of 
Spreading Activation. Journ.ef Verbal Lear- 
nir, g and Verbal Behavior 21(1982) 468-492 
Rieger, B.(1980): Fuzzy Word Meaning Analysis and 
Representation, Proceedings of COLINS 80, Tok- 
yo 1980, 76-84 
Rieger, B.(1981a): Feasible Fuzzy Semantics. Eik- 
meyer/Rieser (Eds.): Words, Worlds, and Con- 
texts. New Approaches to Word Semantics, Ber- 
lin/ NewYork (deSruyter) 1981, 193-209 
Rieger,B.(1981b): Connotative Dependency Structures 
in Seman tic Space. in: Rieger (Ed.): Empiri- 
cal Semantics II, Bochum (Brockmeyer) 1981, 
622-711 
AUGLAND ~ 
'3.04J/.004 \]~ 
HKNDEL 4.7?4/.O02 
B/~t) . tills F 4.650/.000 
~1.983/.OOO 
EkWAH'|'EN KU~Z I-~'4.611/.OO2 1:"'4.U92/.OOO 
J.426/.004 
~KRA/~K ~.NTRAuE N'fEUEH 
2.875/.O57 4.4J5/.013 \[~"4.427/.c.~3 
DIPLOM ";="O.115/.865 
g. 2 
Rieger, B.(1982): Procedural Meaning Representa- 
tion. in: Horecky (Ed.): COLIN8 82. Procee- 
dings of the 9th Intern. Conference on Compu- 
tational Linguistics, Amsterdam/New York 
(North Holland) 1982, 319-324 
Rieger, B.(1983): Clusters in Semantic Space. in: 
Delatte (Ed.): Actes du Congrds International 
Informatique et Sciences Humaines, Universitd 
de Lieges (LASLA), 1983, 805-814 
Rieger, B. (1984a): Semantische Dispositionen. Pro- 
zedurale Wissensstrukturen mit stereotypisch 
repraesentierten Wortbedeutungen. in: Rieger 
(Ed.): Dynamik in der Bedeutungskonstitution, 
Hamburg (Buske) 1983 Kin print) 
Rieger, B.(1984b):Inducing a Relevance Relation in 
a Distance-like Data Structure of Fuzzy Word 
Menanlng Representation. in: Allen, R.F.(Ed.): 
Data Bases in the Humanities and Social Scien- 
ces (ICDBHSS/83), Rutgers University, N.J. 
Amsterdam/NewYork (North Holland) 1984 (in pr) 
Rieger, B.(1984c): Lexikal Relevance and Semantic 
Dispposition. in: Hoppenbrouwes/Seuren/Weij- 
ters (Eds.): Meaning and the Lexicon. Nijmegen 
University (M.I.S. Press) 1984 (in print) 
Zadeh, L.A.(1981): Test-Score Semantics for Natural 
Languages and Meaning Representation via PRUF. 
in: Rieger (Ed.): Empirical Semantics I, Bo- 
chum (Brockmeyer) 1981, 281-349 
301 
