Context sensitive generation of descriptions

Probably the best current algorithm for generating definite descriptions is the Incremental Algorithm due to Dale and Reiter. If we want to use this algorithm in a Concept-to-Speech system, however, we encounter two limitations: (i) the algorithm is insensitive to the linguistic context and thus always produces the same description for an object, (ii) the output is a list of properties which uniquely determine one object from a set of objects: how this list is to be expressed in spoken natural language is not addressed. We propose a modification of the Incremental Algorithm based on the idea that a definite description refers to the most salient element in the current context satisfying the descriptive content. We show that the modified algorithm allows for the context-sensitive generation of both distinguishing and anaphoric descriptions, while retaining the attractive properties of Dale and Reiter's original algorithm.