Paper: Semantic Relevance And Aspect Dependency In A Given Subject Domain: Contents-Driven Algorithmic Processing Of Fuzzy Wordmeanings To Form Dynamic Stereotype Representations

ACL ID P84-1062
Title Semantic Relevance And Aspect Dependency In A Given Subject Domain: Contents-Driven Algorithmic Processing Of Fuzzy Wordmeanings To Form Dynamic Stereotype Representations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1984
Authors

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 pro...