COL1NG ~, Z Horeck: (ed.) 
Noah.Holland Pub~hi~ Comply 
© Ac~lem~ 1982 
PROCEDURAL MEANING REPRESENTATION BY CONNOTATIVE 
DEPENDENCY STRUCTURES. AN EMPIRICAL APPROACH TO 
WORD SEMANTICS FOR ANALOGICAL INEERENCING 
Burgbard B. Rieger 
MESY-Group, German Department 
Technical University of Aachen 
Germany 
Natural language understanding systems make use of language 
and/or worldknowledge bases. One of the salient problems 
of meaning representation and knowledge structure is the 
modelling of its acquisition and modification from natural 
language processing. Based upon the statistical analysis of 
discourse, a formal representation of vague word meanings is 
derived which constitutes the lexical structure of the voca- 
bulary employed in the texts as a fragment of the connotative 
knowledge conveyed in discourse. It consists of a distance- 
like data structure of linguistically labeled space points 
whose positions give a prototype-representation of conceptual 
meanings. On the basis of these semantic space data an algo- 
rithm is presented which transforms prevailing similarities 
of conceptual meanings as denoted by adjacent space points 
to establish a binary, non-symmotric, and transitive relation 
between them. This allows for the hierarchical reorganization 
of points as nodes dependent on a head in a binary tree called 
connotative dependency structure (CDS\]. It offers an empiri- 
cally founded operational approach to determine relevant port- 
ions of the space structure constituting semantic dispositions 
which the priming of a meaning point will trigger with de- 
creasing criteriality. Thus, the CDS allows for the execution 
of associatively guided search strategies, contents-oriented 
retrieval operations, and source-dependent processes of ana- 
logical inferencing. 
INTRODUCTION 
In procedural approaches of linguistic semantics, cognitive psycho- 
logy and artificial intelligence, natural language understanding 
systems make use of language and/or world knowledge bases. Defined 
as lexical structures, memory models or semantic networks, they 
are formatted according to whatever representational, explanatory 
or inferential purpose a particular simulation of processes and/or 
of understanding was aiming at (I). ~he language and world knowledge 
embodied in these systems, however, is restricted under two aspects: 
most of it is obtained introspectively and as such not warranted by 
any operational means or, whenever it seems to, these operations are 
not the permitting condition for, but a performiffg result of simple 
referencing in clear-cut environments. 
Based mainly upon the investigatorS' or the system designers' own or 
some consulted experts' linguistic competence and/or world knowledge 
in a subject domain, the data considered semantically relevant to be 
organized in referential and/or conceptual structures (lists, arrays, 
networks, topologies, etc.) have a more or less ad hoc character and 
are confined to representing logically reconstructable propositions. 
319 
320 B.B. RIEGER 
Neglectable as these shortcomings prove to be for strictly ex- 
tensionally defined environments and fragments of knowledge structure 
in referential models, data complexity tends to increase to meet ex- 
ploding difficulties and escalating problems whenever abstract concepts 
or even vague meanings are to be processed in a not exclusively de- 
notative but also connotative setting of formal semantic representa- 
tion. 
As natural language communication may be characterized by the appar- 
ent ease and efficienc~, however, with which ill-defined concepts 
and fuzzy meanings are being intended and expressed by speakers, id- 
entified and understood by hearers, and successfully used by speakers 
/hearers in performing inferences of some - not necessarily logical - 
sort, it is argued here, that any non-trivial simulation of processes 
of cognition and/or natural language comprehension will have to pro- 
vide some means of dynamic knowledge representation which permits to 
account more satisfactorily for one or the other aspect raised above. 
The concept of 'representation of knowledge' seems lucid enough 
when talking about memories of sentences, numbers,or even faces, 
for one can imagine how to formulate these in terms of proposi- 
tions, frames, or semantic networks. But it is much harder to do 
this for feelings, insights and understandings, with all the atti- 
tudes, dispositions, and 'ways of seeing things' that go with them. 
\[The term 'disposition' is used here in its ordinary language 
sense to mean 'a momentary range of possible behaviours'\]) Tradi- 
tionally, such issues are put aside, with the excuse that we should 
understand simpler things first. But what if feelings and view- 
points a r e the simpler things - the elements of which the 
others are composed? Then, I assert, we should deal with disposi- 
tions directly, using a 'structural' approach ... (2) 
In the present case this has been developed in two stages: the seman- 
tic space as a distance-like data structure, and an algorithm to trans- 
form its distance relations to form source-oriented hierarchies of 
connotative dependency structures. 
SEMANTIC SPACE STRUCTURE 
Theoretical approaches in formal semantics tend to deny a dynamic 
linguistic meaning structure, but assume the existence of an external 
system structure of a world, o~ possible worlds, whose pre-formatted 
entities may referentially be related to language terms constituting 
their denotation. Structural approaches in linguistic semantics tend 
to deny the possibility of denotational, but presuppose the knowledge 
(and comprehension) of language systems whose semantic relations among 
their items are being described intra-lingually by means of syntag- 
matic and paradigmatic oppositions along certain dimensions in seman- 
tic fields. Other than these two, our present way of approach strives 
to presuppose as little and to reconstruct empirically as much as 
possible of the relational (not necessarily logically reconstructable) 
structure that in the course of discourse is constituted by the regu- 
lar use of language terms as a system of linguistically labeled em- 
pirical objects, called meanings. 
We consider the natural language users' ability to intend and compre- 
hend meanings in verbal interaction a phenomenologically undoubtable, 
empirically well established, and theoretically defensible basis for 
any semantic study of natural language performance. It is assumed 
that the usage regularities followed and/or established by employing 
different lexical items differently for communicative purposes in 
discourse may be analysed not only to describe the lexical structure 
of vocabulary items used, but also to model a fragment of the con- 
EMPIRICAL APPROACH TO WORD SEMANTICS FOR ANALOGICAL INFERENCING 321 
comitantly conveyed common knowledge or semantic memory structure 
constituted. 
This is achieved by an algorithm that takes lemmatized strings of 
natural language disc6urse of a certain domain as input and produces 
as output a distance-like data structure of linguistically labeled 
points whose positions represent their meanings. As the statistical 
means for the empirical analysis of prevailing interdependencies bet- 
ween lexical items in text strings have elsewhere (3) been developed 
and discussed to some extent (4) 9 and as the formal representation 
of vague word meanings derived from these analyses has previously (5) 
been outlined and illustrated~ too (6), an informal description will 
suffice here. 
The algorithm applied so far consists of a consecutive mapping of 
lexical items onto fuzzy subsets of the vocabulary according to the 
numerically specified statistical regularities and the differences~ 
these items have been used with in the discourse analysed. The re- 
sulting system of sets of fuzzy subsets may be interpreted topologi- 
cally as a n-dimensional hyperspace with a natural metric. Its n 
linguistically labeled elements (representing meaning points) and 
their mutual distances (representing meaning differences) form dis- 
cernable clouds and clusters (7). These determine the overall struc- 
turedness of a domain by measurable semantic (paradigmatic and/or 
syntagmatic) properties of the lexical items concerned. 
CONNOTATIVE DEPENDENCY STRUCTURE 
Stimulated by the theory of spreading activation in memory models (8) 
in conjunction with the psyhhological account of language understand- 
ing in procedural semantics (9) a dynamic meaning representation can 
be developed of the basis of the prototypical, but static represen- 
tations provided by the semantic hyperspace strucure. This is mchiev- 
ed by a recursively defined algorithm which has formally been intro- 
duced elsewhere (Io) so that it may verbally be described here as a 
procedure to generate a potential of latent relations among meaning 
points in the semantic space. 
In a way9 this procedure reconstructs for this model what recent 
theories of cognition and language comprehension have introduced in 
network models of semantic memory: paths of excitation that may be 
activated from any primed node and which spread along node relating 
links over the whole network with decreasing intensities. Compared 
to the execution of spreading activation processes in network models~ 
however, the present procedure - speaking in model genetical terms - 
must be considered of prior status. The semantic hyperspace is not a 
transitively related network of nodes, but a symmetrically related 
data structure of linguistically labeled n-tuples of numerical values. 
Therefore, priming of any item would immediately activate every other 
item rendering the process of spreading activation undiscriminating 
for semantic representation. So, the new procedure, first, has to 
establish links between items and evaluate them by processing the 
data base provided in order to let these links eventually serve as 
directed paths along which possible activation might spread. 
Operating on the distance-like data of the semantic space, the al- 
gorithm's generic procedure will start with any meaning point being 
primed to determine those two other points, the sum of distances bet- 
ween which form a triangle of minimum edges' lengths. Repeated success- 
ively for each of these meaning points listed and in turn primed in 
accordance with this procedure, particular fragments of the relational 
structure inherent in the semantic space will be selected depending 
on the aspect, i.e. the primed point the algorithm is initially started 
with. Working its way through and consuming all labeled points in the 
space system, the procedure transforms prevailing similarities of 
322 B.B. RiEGER 
meanings as represented by adjacent points ~o establish a binary, 
non-symmetric, and transitive relation between them~It allows for 
the hierarchical rearrangement of meaning points as nodes under a 
primed head in the format of a binary tree~ called connotative de- 
pendency structure (CDS) o 
The process of detection and identification which the algorithm per- 
formes may be illustrated in view of a t~?o-.dimensionel space confi- 
guration of 11 points < d{a,b,c~dge~f~g~h~i,~,k}> (Fig. I). 
Fig. \] 
e 
d b "s 
k J 
g 
h 
Fig. 2 ///~ x" , 
a 
e b 
x c 3 
Fig. 3 
Submitted to the search procedure of least 
triangle under initial priming of the point a 
the algorithm will identify the number of 
triangles in Fig. 2 and produce thebinary 
tree as shown in Fig. 3. For the effective use 
in procedural meaning representation and son, an- 
tic Drocessing~ the CDS-trees may additional- 
ly be evaluated by connotative c~iterialities 
(1o). The criteriaiity is a numerical express- 
ion of the degree or intensity by whicb any 
COS-node is dependent on the head; c%!cula~ed 
as ~ funhtion both of the involved meaning 
points ~ topology and their relative distaDc~s 
in the semantic space° The head's criteriality being I.Oo~ this value 
is splitted among every two dependent nodes~ and consequently decreases 
from level to level in the tree structure approximating O. 
Examples of connotative dependency trees are given below where the 
upper fragments of the COSts of ARBEIT/labour (Fig.4) and INDUSTRIE/ 
industry (Fig. 5) are shown as computed from the semantic space shruc- 
ture derived of a sample of German newspaper texts from the 1964 daily 
editions of 'Die Welt' 
It goes without saying that the generating of CDS-trees is a prerequi- 
sit to source-oriented search and retrieval procedures which may thus 
be performed effectively on the semantic space structure. Given, say~ 
the meaning point ARBEIT/!abour to be primed, ands say~ INDUSTRIE/in- 
dustry as the target point to be searched~ the COS (ARBEIT) wiT~ be 
generated first. It provides semantic dispositions of decreasing lri- 
teriality under the aspect of ARBEIT in ~he semantic space data. Then, 
the tree will be searches Cbreadth-fJrst) for the target node INDUSTRIE, 
!~hen this is hit, its dependency path will be activated to back-track 
those intermediate nodes which 6otermine the connotative transitions 
EMPIRICAL APPROACH TO WORD SEMANTICS FOR ANALOGICAL INFERENCING 323 
of INDUSTRIE under the aspect of ARBEIT, namely UNTERNEHMEN/business, 
STADT/town, ANGEBOT/offer as underlined in Fig. 4. 
The priming of INDUSTRIE and the targetting of ARBEIT leads to the 
activation of quite a different dependency path mediating ARBEIT un- 
der the aspect of INDUSTRIE, namely by KENNTNIS/knowledge, ERFAHR/ 
experience, LEIT/control, as underlined in Fig. 5. 
Fig. 4 
I.OOO ARBEIT 
.086 INDUSTRIE 
.169 UNTERNEEM \] i 
QO~3 SUCH 
.290 STADT J 
.062 SC~EIB 
.121WUNSCH 
.059 SCHUL 
.558 ANGE~ 
.075 LEHR 
.141VERKEHR 
.066 GESCHKFT 
.268, GEBIET 
.O78 VERWALT 
.127 EINSATZ 
.049 WIRTSCHAFT 
.076 BERUF 
.151 STELLE 
.075 ELEKTRON 
.276 AUSGABE 
P .......... "I .065 UNTERRICHT 
' ~.125 ORGANISAT 1 
I .O60 BITTE I 
~E_:.=~. _. ~ o53 H~'~RSC~T 
.088 ALLGEMEIN 
.035 MODE 
.166 VERBAND 
.O40 AUSLAND 
.078 VERANTWORT 
.038 FOLGE 
Fig. 5 
T 5o'o- £~66~£E .... " 
.290 ELEKTRON 
.150 SCHUL 
.130 SCHREIE 
.536 SUCH f ............ 
! t 
I .137 UNTERRICHT I 
! .246 BERUF ' L 
p ........... ~ 
' H .1o90RG~XSAT I 
.464 LEIT 
.283 COMPUTER 
.151FKHIG 
• 132 SYSTEM 
.115 O IPLOM 
• 181 ERFAHR 
.066 KENNTNIS 
.075 GEBIET 
.O75: VERBAND 
.073 UNTERNE~M 
.067 STADT 
.105 BITTE 
.032 TECHNIK 
.058 STELLE 
.O51 WUNSCH 
.080 AU SGABE 
.O71 ALLGEME IN 
.068 PERSON 
.064 ANGEBOT 
.059 VERKEHR 
.056 EINSATZ 
.035 ARBEIT 
~.O31 VERANTWORT 
Using these source-oriented search and retrieval processes, an ana-, 
logical, contents-dependent form of inferencing, as opposed so logi- 
cal deduction,may operationally be devised by way of parallel pro- 
cessing of two ~or more) COS-trees. For this purpose an algorithm is 
started by the two (or more) meaning premises of, say, ARBEIT and 
324 B.B. RIEGER 
INDUSTRIE. Their CDS-trees will be generated before the inferencing 
procedure begins to work its way (breadth-first) through the trees' 
levels, taking highest criterialities first in tagging each encounter- 
ed node. When the first node is met which has previously been tagged 
already, the search procedure stops to activate the dependency paths 
from this concluding common node - here, ORGANISAT/organization - 
in the CDS-trees concerned, as illustrated in Fig. 4 and Fig. 5 by 
dotted lines, separately presented in Fig. 6. 
1.0 
1 
8 
9 
lo 
ARBEIT INDUSTRIE 1.O 
.442 Person Such .536 
.276 Ausgabe Beruf .246 / 
.125 \[ ORGANISATIONJ .109 
Fig. 6 

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