Improving KNN Arabic Text Classification with N-Grams Based Document Indexing

Text classification is the task of assigning a document to one or more of pre-defined categories based on its contents. This paper presents the results of classifying Arabic language documents by applying the KNN classifier, one time by using N-Gram namely unigrams and bigrams in documents indexing, and another time by using traditional single terms indexing method (bag of words) which supposes that the terms in the text are mutually independent which is not the case. Results show that using N-Grams produces better accuracy than using Single Terms for indexing; the average accuracy of using N-grams is .7357, while with Single terms indexing the average accuracy is .6688.