Attribute-Based and Value-Based Clustering: An Evaluation.

Abstract

In most research on concept acquisition from corpora, concepts are modeled as vectors of relations extracted from syntactic structures. In the case of modifiers, these relations often specify values of attributes, as in (attr red); this is unlike what typically proposed in theories of knowledge representation, where concepts are typically defined in terms of their attributes (e.g., color). We compared models of concepts based on values with models based on attributes, using lexical clustering as the basis for comparison. We find that attribute-based models work better than value-based ones, and result in shorter descriptions; but that mixed models including both the best attributes and the best values work best of all.

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