Using Progressive Filtering to Deal with Information Overload

Abstract

In the age of Web 2.0 people organize large collections of web pages, articles, or emails in hierarchies of topics, or arrange a large body of knowledge in ontologies. This scenario requires automatic text categorization systems able to cope with underlying taxonomies in an effective and efficient way, so that information overload and input imbalance can be suitably dealt with. In this work, we propose a hierarchical text categorization approach that decomposes a given rooted taxonomy into pipelines, one for each path that exists between the root and each node of the taxonomy, so that each pipeline can be tuned in isolation. Experimental results, performed on Reuters and DMOZ data collections, show that the proposed approach performs better than a flat approach in presence of input imbalance.