Hierarchical Text Categorization Through a Vertical Composition of Classifiers

Authors:
Andrea Addis;Giuliano Armano;Francesco Mascia;Eloisa Vargiu
Affiliations:
University of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy;University of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy;University of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy;University of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy
Venue:
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Year:
2007

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Abstract

In this paper we present a hierarchical approach to text categorization aimed at improving the performances of the corresponding tasks. The proposed approach is explicitly devoted to cope with the problem related to the unbalance between relevant and non relevant inputs. The technique has been implemented and tested by resorting to a multiagent system aimed at performing information retrieval tasks.