Most AI systems model and represent natural concepts and categories using uniform taxonomies,in which no level in the taxonomy is distinguished. W e present a representation of natural taxonomies based on the theory that human category systems are non-uniform.That is, not all levels of abstraction are equally important or useful; there is a basic level which forms the core of a taxonomy. Empirical evidence for this theory is discussed, as are the linguistic and processing implications of this theory for an artificial intelligence/natural language processing system. We present our implementation of this theory in SNePS, a semantic network processing system which includes an A T N parser generator,demonstrating how this design allows our system to model human performance in the natural language generation of the most appropriate category name for an object.The internal structure of categories is also discussed, and a representation for natural concepts using a prototype model is presented and discussed.

Representations for natural category systems and a retrieval-based framework are presented that provide the means for applying generic knowledge about the semantic relationships between entities in discourse and the relative salience of these entities imposed by the current context. An analysis of the use of basic and superordinate level categories in discourse is presented, and the use of our representations and processing in the task of discourse comprehension is demonstrated.