Proceedings of the Annual Meeting of the Cognitive Science Society

I present a computationally-based representation for indefinite noun phrases and anaphora that models their use in natural language. To this end, three goals for knowledge representation for natural language processing: natural form, conceptual completeness, and structure sharing are described. In addressing these goals, an augmentation to the representation of variables (corresponding to indefinite noun phrases or anaphora) so that variables are not atomic terms is suggested. This leads to an extended, more "natural" representation. It is shown how this representation resolves some representational difficulties with sentences with nonlinear quantifier scoping, in particular, donkey sentences.