Generating Natural Language Definitions from Classification Hierarchies

Mark T. Maybury


In interactions with users, knowledge based systems are often called upon to define their terms or concepts [Maybury, 1989]. These terms and concepts usually comprise classes within some classification scheme (e.g., a generalization hierarchy). Beyond simply retrieving the superclass of the to-be-defined class (e.g., "a mammal is a vertebrate") a more sophisticated definition also requires selection of distinguishing features or characteristics of this class (e.g., "a mammal is a vertebrate that gives live birth to and nurses its offspring"). To do this, we have refined and extended set theoretic, feature-based models of object similarity and proWtypica1ity, and developed an algorithm that selects the most distinguishing set of attributes and attribute-value pairs of a class in the context of a taxonomy of classes and their properties based on notions of prototypicality and discriminatory power. In this paper, we illustrate a classificatory representation using objects and attribute-value pairs in a test domain of vertebrates; describe our algorithm for computing prototypicality, discriminatory power, and distinctive power, based on this sample representation; and show how this algorithm is implemented to generate definitions of object classes in this representation.

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