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Semiotics and Computational Linguistics

On Semiotic Cognitive Information Processing
  • Burghard B. Rieger
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 33)

Abstract

Signs, which are the domain of inquiry in semiotics, have a complex ontology. Apart from being used—adequate knowledge provided—by communicators, and recognized as being decomposable into smaller elements and aggregatable to larger structures, they are also meant to be understood. This is a consequence of their manifold identity as compound physical objects with real world extensions in space-time-locations and as activators for complex mental processes which tend to be identified with some mind and/or brain activities responsible for their understanding. In the cognitive sciences all processes of perception, identification, and interpretation of (external) structures are considered information processing which (natural or artificial) systems—due to their own (internal) structuredness or knowledge—are able (or unable) to perform. Combining the semiotic with the cognitive paradigm in computational linguistics, the processes believed to constitute natural language sign structures and their understanding is modeled by way of procedural, i.e. computational (re-)constructions of such processes that produce structures comparable to those that the understanding of (very large) samples of situated natural language discourse would imply. Thus, computational semiotic models in cognitive linguistics aim at simulating the constitution of meanings and the interpretation of signs without their predicative and propositional representations which dominate traditional research formats in syntax and semantics so far. This is achieved by analyzing the linear or syntagmatic and selective or paradigmatic constraints which natural languages impose recursively on the formation and structure of (strings of) linguistic entities on different levels of systemic distinction. It will be argued (and illustrated) that fuzzy modeling allows to derive more adequate representational means whose (numerical) specificity and (procedural) definiteness may complement formats of categorial type precision (which would appear phenomenologically incompatible) and processual determinateness (which would seem cognitively inadequate). Several examples from fuzzy linguistic research will be given to illustrate these points.

Keywords

Natural Language Minimal Span Tree Computational Linguistic Semantic Space Statistical Machine Translation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Burghard B. Rieger
    • 1
  1. 1.FB II: Department of Computational LinguisticsUniversity of TrierGermany

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