Meaning Representation in Natural Language Categorization


A large number of formal models of categorization have been proposed in recent years. Many of these are tested on artificial categories or perceptual stimuli. In this paper we focus on categorization models for natural language concepts and specifically address the question of how these may be represented. Many psychological theories of semantic cognition assume that concepts are defined by features which are commonly elicited from humans. Norming studies yield detailed knowledge about meaning representations, however they are small-scale (features are obtained for a few hundred words), and admittedly of limited use for a general model of natural language categorization. As an alternative we investigate whether category meanings may be represented quantitatively in terms of simple co-occurrence statistics extracted from large text collections. Experimental comparisons of feature-based categorization models against models based on data-driven representations indicate that the latter represent a viable alternative to the feature norms typically used.

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