Cross-Comparison for Two-Dimensional Text Categorization

The organization of large text collections is the main goal of automated text categorization. In particular, the final aim is to classify documents into a certain number of pre-defined categories in an efficient way and with as much accuracy as possible. On-line and run-time services, such as personalization services and information filtering services, have increased the importance of effective and efficient document categorization techniques. In the last years, a wide range of supervised learning algorithms have been applied to this problem [1]. Recently, a new approach that exploits a two-dimensional summarization of the data for text classification was presented [2]. This method does not go through a selection of words phase; instead, it uses the whole dictionary to present data in intuitive way on two-dimensional graphs. Although successful in terms of classification effectiveness and efficiency (as recently showed in [3]), this method presents some unsolved key issues: the design of the training algorithm seems to be ad hoc for the Reuters-21578 collection; the evaluation has only been done only on the 10 most frequent classes of the Reuters-21578 dataset; the evaluation lacks measure of significance in most parts; the method adopted lacks a mathematical justification. We focus on the first three aspects, leaving the fourth as the future work.

Authors and Affiliations

1. Department of Information Engineering, University of Padua,  

   Giorgio Maria Di Nunzio

Editors and Affiliations

1. Georgia Institute of Technology and Università di Padova,  

   Alberto Apostolico

2. Department of Information Engineering, University of Padova,  

   Massimo Melucci

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