Semantic Relevance and Aspect Dependency in a Given Subject Domain: Contents-driven algorithmic processing of fuzzy wordmeanings to form dynamic stereotype representations

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 s t a t i s t i c a l l y , 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 s t i l l 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 ( l i s t s . 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 f l e x i b i l i t y and dynamics are needed for automatic meaning representation f r o m 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 …

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