Human categorization is neither a binary nor a context-free process. Rather, some concepts are better examples of a category than others, while the criteria for category membership may be satisfied to different degrees by different concepts in different contexts. In light of these empirical facts, WordNet’s static category structure appears both excessively rigid and unduly fragile for processing real texts. In this paper we describe a syntagmatic, corpus-based approach to redefining WordNet’s categories in a functional, gradable and context-sensitive fashion. We describe how the diagnostic properties for these definitions are automatically acquired from the web, and how the increased.
