Generating Vague Descriptions

This paper deals with the generation of definite (i.e., uniquely referring) descriptions containing semantically vague expressions ('large', 'small', etc.). Firstly, the paper proposes a semantic analysis of vague descriptions that does justice to the contextdependent meaning of the vague expressions in them. Secondly, the paper shows how this semantic analysis can be implemented using a modification of the Dale and Reiter (1995) algorithm for the generation of referring expressions. A notable feature of the new algorithm is that, unlike Dale and Reiter (1995), it covers plural as well as singular NPs. This algorithm has been implemented in an experimental NLG program using ProFIT. The paper concludes by formulating some pragmatic constraints that could allow a generator to choose between different semantically correct descriptions. 1 I n t r o d u c t i o n : V a g u e p r o p e r t i e s a n d G r a d a b l e A d j e c t i v e s Some properties can apply to an object to a greater or lesser degree. Such continuous, or vague properties, which can be expressed by, among other possibilities, gradable adjectives (e.g., 'small', 'large', e.g. Quirk et al. 1972 sections 5.5 and 5.39), pose a difficult challenge to existing semantic theories, theoretical as well as computational. The problems are caused partly by the extreme context-dependence of the expressions involved, and partly by the resistance of vague properties to discrete mathematical modeling (e.g., Synthese 1975, Pinkal 1995). The weight of these problems is increased by fact that vague expressions are ubiquitous in many domains. The present paper demonstrates how a Natural Language Generation (NLG) program can be enabled to -generate uniquely referring descriptions containing one gradable adjective, despite the vagueness of the adjective. Having presented a semantic analysis for such vague descriptions, we describe the semantic core of an NLG algorithm that has numerical data as input and vague (uniquely referring) descriptions as output. One property setting our treatment of vagueness apart from that in other NLC programs-(e.g. Goldberg 1994) is that it uses ••vague properties for an exact task, namely the ruling out of distractors in referring expressions (Dale and Reiter 1995). Another distinctive property is that our account allows the 'meaning' of vague expressions to be determined by a combination of linguistic context (i.e., the Common Noun following the adjective) and nonlinguistic context (i.e., the properties of the elements in the