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

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 / is present in a category c\ the Expressiveness E, how much / 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 Fl-measure of 0.89, with a peak of 0.899. Moreover, a macro-averaged Recall and Precision was calculated: the former reported a 0.72, the latter a 0.79. These results reach most ...

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Giorgio Maria Di Nunzio, Alessandro Micarelli
• Keywords: IJCAI 2003, IJCAI 2007, Supervised Learning Algorithm, Text Categorization, Text Categorization Problem