Does a New Simple Gaussian Weighting Approach Perform Well in Text Categorization?

Abstract

A new approach to the Text Categorization problem is here presented. It is called Gaussian Weighting and it is a supervised learning algorithm that, during the training phase, estimates two very simple and easily computable statistics which are: the Presence P, how much a term tis present in a category c; the Expressiveness E, how much t is present outside c in the rest of the domain. Once the system has learned this information, a Gaussian function is shaped for each term of a category, in order to assign the term a weight that estimates the level of its importance for that particular category. We tested our learning method on the task of single-label classification using the Reuters-21578 benchmark. The outcome of the result was quite impressive: in different experimental setups, we reached a microaveraged F1-measure of0 .89, with a peak of0 .899. Moreover, a macro-averaged Recall and Precision was calculated: the former reported a 0.72, the latter a 0.79. These results reach most of the stateof-the-art techniques of machine learning applied to Text Categorization, demonstrating that this new weighting scheme does perform well on this particular task.