Meaning acquisition by SCIPS

Abstract

The emergence of semantic structure as a self-organizing process is studied in semiotic cognitive information processing systems on the basis of word usage regularities in natural language discourse whose linearly agglomerative (syntagmatic) and whose selectively interchangeable (paradigmatic) constraints are exploited by text analysing algorithms. They accept natural language discourse as input and produce a vector space structure as output which may be interpreted as an internal (endo) representation of the SCIP system's states of adaptation to the external (exo) structures of its environment as mediated by the discourse processed. In order to evaluate the sytem's endo-representation against the exo-view of its environment as described by the natural language discourse processed, a corpus of texts-composed of correct and true sentences with well-defined referential meanings-was generated according to a (very simple) phrase structure grammar and a fuzzy referential semantics which interpret simple composite predicates of cores (like: on the left, in front etc.) and hedges (like: extremely nearby, very faraway etc.). Processed during the system's training phase, the corpus reveals structural constraints which the system's hidden structures or internal meaning representations apparently reflect. The system's architecture is a two-level consecutive mapping of distributed representations of systems of (fuzzy) linguistic entities whose states acquire symbolic functions that can be equaled to (basal) referencial predicates. Test results from an experimental setting with varying fuzzy interpretations of hedges are produced to illustrate the SCIP system's miniature (cognitive) language understanding and meaning acquisition capacity without any initial explicit syntactic and semantic knowledge.