Experimenting N-Grams in Text Categorization

This paper deals with automatic supervised classification of documents. The approach suggested is based on a vector representation of the documents centred not on the words but on the n-grams of characters for varying n. The effects of this method are examined in several experiments using the multivariate chi-square to reduce the dimensionality, the cosine and Kullback Liebler distances, and two benchmark corpuses the reuters-21578 newswire articles and the 20 newsgroups data for evaluation. The evaluation was done, by using the macroaveraged F1 function. The results show the effectiveness of this approach compared to the Bag-Of-Word and stem representations.