Clustering documents into a web directory for bootstrapping a supervised classification

This paper proposes some solutions for the bootstrapping problem, that implicitly or explicitly use taxonomy definition: a baseline approach that classifies documents according to the class terms, and two clustering approaches, whose training is constrained by the a priori knowledge encoded in the taxonomy structure, which consists of both terminological and relational aspects. In particular, we propose the TaxSOM model, that clusters a set of documents in a predefined hierarchy of classes, directly exploiting the knowledge of both their topological organization and their lexical description. Experimental evaluation was performed on a set of taxonomies taken from the Google™ and LookSmart™ web directories, obtaining good results.