Burghard B. Rieger
Discourse Understanding as Image
Generation.
On perception-based processing
of NL texts in SCIP systems.
In: Al-Dabass, D. (eds.): Proceedings of the 6th National
conference of the United Kingdom Simulation Society (UKSim 2003), Emmanuel College Cambridge, Nottingham (NTU)
2003, pp. 1-8
Abstract
Semiotic Cognitive Information Processing (SCIP) is inspired by information systems theory
and grounded in (natural/artificial) system-environment situations. SCIP systems’ knowledge-based
natural language processing (NLP) of
information makes it
cognitive, their sign and
symbol generation, manipulation, and understanding capabilities render it
semiotic. Based
upon structures whose representational status is not a presupposition to, but a result from recursive
processing, SCIP algorithms initiate and modify the structures they are operating, and by simulating
processes of symbol grounding they realize
meaning constitution and
language understanding.
Whereas traditional semantics is based upon the symbolic (de)composition of propositional structures,
SCIP tries to model
learning and
understanding dynamically by visualizing what is
understood in a perception-based, sub-symbolic, multi-resolutional way of processing natural language
discourse. An experimental 2-dim scenario with object locations described relative to a mobile agent’s
varying positions allows to test SCIP systems’ performance against human natural language understanding
in a controlled way.
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