Computational Semiotics and Fuzzy

Whereas most cognitive approaches in the study of language have been developing hypotheses concerning the principles of knowing and understanding natural languages (i.e. competence) without bothering too much about communicative language usages in realworld situations (i.e. performance), new semiotic approaches in cognitive computational linguistics explore the procedures believed to underlie processes of language learning and understanding. They do so by simulating these capabilities as system behaviour under recourse to mod-eled structures, observable in very large samples of situated natural language discourse and represented in vector space formats via numerically speciied by quantitative methods of dynamic (reconstruction n. It will be argued that the ecological understanding of informa-tional systems in Computational Semiotics corresponds well to the procedural modeling and numerical reconstruction of processes that simulate the constitution of meanings and the interpretation of signs (semiosis).The theories of fuzzy sets 24] and possibility distributions 23] together with their derivatives in soft computing 25] appear to be promising in providing suitable formats for computational approaches to natural language processing without the obligation neither to reject nor to accept traditional formal and modeltheoretic concepts or ontologies. Examples from fuzzy linguistic research 10] 18] will be given to illustrate these points. For the majority of researchers in knowledge representation and natural language semantics the common ground and widely accepted frame for their modeling may be found in the dualism of the rationalistic tradition of thought as exempliied in its matter-mind notion of an independent (objective) reality and some (subjective) conception of it.