{"type":"ScholarlyArticle","title":"Does a New Simple Gaussian Weighting Approach Perform Well in Text Categorization?","authors":[{"name":"Giorgio Maria Di Nunzio","slug":"giorgio-maria-di-nunzio","given":"Giorgio Maria","family":"Di Nunzio","affiliation":""},{"name":"Alessandro Micarelli","slug":"alessandro-micarelli","given":"Alessandro","family":"Micarelli","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2003/86","pdf":"https://ijcai.org/proceedings/2003/0086.pdf","markdown":"https://ijcai.org/proceedings/2003/86.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/86","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":86,"pages":{"first":581,"last":585},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}