Modelling system structures of word meanings and/or world knowledge is to face the problem of their mutual and complex relatedness. 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. In a rather sharp departure from that form of data acquisition the present approach has been based on the empirical analysis of discourse that real speakers/writers

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... oduce in actual situations of performed or intended communication in prescriptive contexts or subject domains. The approach makes essential use of statistical means to analyse usage regularities of words to map their fuzzy meanings and connotative interrelations in a format of stereotypes. Their dependencies are generated algorithmically as multi-perspective dispositions that render only those relations accessible to automatic processing which can -under differing aspects differently -be considered relevant. Generating such semantic dispositional dependencies dynamically by an procedure would seem to be an operational prerequisite to and a promising candidate for the simulation of contents-driven (analogically-associative), instead of formal (logically-deductive) inferences in semantic processing. * This paper (an intermediate version of which was read on ICCH/83) reports on the empirical foundations of a project in computational semantics on the automatic analysis and representation of natural language meanings in texts.