Semantic relevance and aspect dependency in a given subject domain
Burghard B. Rieger · 1984
Cognitive principles underlying the (re-) construction of word meaning and/or world knowledge structures are poorly understood yet. In a rather sharp departure from more orthodox lines of introspective acquisition of structural data on meaning and knowledge representation in cognitive science, an empirical approach is explored that analyses natural language data statistically, represents its numerical findings fuzzy-set theoretically, and interprets its intermediate constructs (stereotype meaning points) topologically as elements of semantic space. As connotative meaning representations, these elements allow an aspect-controlled, contents-driven algorithm to operate which reorganizes them dynamically in dispositional dependency structures (DDS-trees) which constitute a procedurally defined meaning representation format.