Generating Dependency Structures of Fuzzy Word Meanings in Semantic Space

This paper will report on one of the central objectives of a project in computational semantics which is supported by the Northrhine-Westphalia Ministry of Science and Research under grant IV A 2-FA 8600. 1. Modelling system structures of word meanings and/or world knowledge is to face the problem of their mutual and complex relatedness. Under the notion of semantic relevance and knowledge disposition this interdependency may empirically be reconstructablefrom natural language discourse although most approaches in linguistic semantics and artificial intelligence do not address these issues. Instead, linguists as well as experts engaged in word meaning and/or world knowledge representation still provide the necessary semantic or external world data introspectively by exploring their own competence and memory capacities to depict their findings in some semantic or conceptual structures (lists, arrays, networks, etc.). They do so with the understanding that their models may have a more or less ad hoc character and tend to lack --- beyond their limited operational performance --- intersubjective control. Other than these introspective explorations, the present approach strives to derive directly via automatic analysis of natural language discourse some basic data whose relational structure will not be declared but procedurally be defined by algorithms which induce it. 2. Based upon statistical means for the empirical analysis of discourse and for the formal representation of vague word meanings in natural language texts, procedures have been devised which allow for the systematic modelling of a fragment of the lexical structure constituted by the vocabulary employed in the texts as part of the concomitantly conveyed world knowledge concerned [3]. The coefficients applied wi..