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IJCAI
2003

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

9 years 5 months ago
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 ...
Giorgio Maria Di Nunzio, Alessandro Micarelli
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Giorgio Maria Di Nunzio, Alessandro Micarelli
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