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<Paper uid="E06-1041">
  <Title>Structuring Knowledge for Reference Generation: A Clustering Algorithm</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The problem of Generating Referring Expressions (GRE) can be summed up as a search for the properties in a knowledge base (KB) whose combination uniquely distinguishes a set of referents from their distractors. The content determination strategy adopted in such algorithms is usually based on the assumption (made explicit in Reiter (1990)) that the space of possible descriptions is partially ordered with respect to some principle(s) which determine their adequacy.</Paragraph>
    <Paragraph position="1"> Traditionally, these principles have been defined via an interpretation of the Gricean maxims (Dale, 1989; Reiter, 1990; Dale and Reiter, 1995; van Deemter, 2002)1. However, little attention has been paid to contextual or intentional influences on attribute selection (but cf. Jordan and Walker (2000); Krahmer and Theune (2002)). Furthermore, it is often assumed that all relevant knowledge about domain objects is represented in the database in a format (e.g. attribute-value pairs) that requires no further processing.</Paragraph>
    <Paragraph position="2"> This paper is concerned with two scenarios which raise problems for such an approach to GRE: 1. Real-valued attributes, e.g. size or spatial coordinates, which represent continuous dimensions.</Paragraph>
    <Paragraph position="3"> The utility of such attributes depends on whether a set of referents have values that are 'sufficiently  hasbeen interpreted asadirectivetofindtheshortest possible description for a given referent close' on the given dimension, and 'sufficiently distant' from those of their distractors. We discuss this problem inSS2.</Paragraph>
  </Section>
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