Implications of Natural Categories for Natural Language Generation
Cline, Ben E.
Nutter, J. Terry
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Psychological research has shown that natural taxonomies contain a distinguished or basic level. Adult speakers use the names of these categories most frequently and can list a large number of attributes for them. They typically can list many attributes for superordinate categories and list few additional attributes for subordinate categories. Because natural taxonomies are important to human language, their use in natural language processing systems appears well founded. In the past, however, most AI systems have been implemented around uniform taxonomies in which there is no distinguished level. It has recently been demonstrated that natural taxonomies enhance natural language processing systems by allowing selection of appropriate category names and by providing the means to handle implicit focus. We propose that additional benefits from the use of natural categories can be realized in multi-sentential connected text generation systems. After discussing the psychological research on natural taxonomies that relates to natural language processing systems, the use of natural categorizations in current natural language processing systems is presented. We then describe how natural categories can be used in multiple sentence generation systems to allow the selection of appropriate category names, to provide the mechanism to help determine salience to aid in the selection of discourse schema. to provide for the shallow modeling audience expertise, and to increase the efficiency of taxonomy inheritance.