Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution

Abstract

Human beings appear to be very particular information processing systems whose outstanding plasticity and capability to cope with changing environmental conditions (adaptation) is essentially tied to their use of natural languages in communication to acquire knowledge (learning). Their knowledge based processing of information makes them cognitive,and their sign and symbol generation, manipulation, and understanding capabilities render them semiotic. Semiotic cognitive information processing (SCIP) is inspired by information systems theory according to which living systems process and structure environmental data according to their own structuredness. When these processes are modeled as operating on structures whose representational status is not so much a presupposition to but rather a result from such processing, then the resulting models --- being able to simultaneously instanciate, create and/or modify these structures --- may attain a quality of sign and symbol understanding which may computationally be realized. This quality will in the sequel be studied and identified as a particular form of knowledge acquisition or learning whose results can be visualized as incremental dynamics of structure formation. Its formal delineation, operational specification, and algorithmic implementation allows for experimental testing of the SCIP system's capability for meaning constitution from natural language texts without prior morphological, lexical, syntactic and/or semantic knowledge.