Implications of the Recursive Representation Problem for Automatic Concept Identifcation in On-line Governmental lnformation

Miles Efron, Gary Marchionini, Julinang Zhiang


This paper describes ongoing research into the application of unsupervised learning techniques for improving access to governmental information on the Web. Under the auspices of the GovStat Project (, our goal is to identify a small number of semantically valid and mutually exclusive "concepts" that adequately span the intellectual domain of a web site. While this is a classic instance of the clustering problem [14] the task is complicated by the dual-representational nature of term-document relationships. Since documents are defined in term-space and vice versa, we may approach this as a document-or term-clustering problem. The current study explores the implications of pursuing both term- and document-centered representations. Based on initial work, we argue for a document clustering-based approach. Describing completed research, we suggest that term clustering yields semantically valid categories, but that these categories are not suitably broad. To improve the coverage of the clustering, we describe a process based on document clustering.

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