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Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies

Abstract : The aim of this study is topic identification by using two methods, in this case, a new one that we have proposed: TR-classifier which is based on computing triggers, and the well-known k Nearest Neighbors. Performances are acceptable, particularly for TR-classifier, though we have used reduced sizes of vocabularies. For the TR-Classifier, each topic is represented by a vocabulary which has been built using the corresponding training corpus. Whereas, the kNN method uses a general vocabulary, obtained by the concatenation of those used by the TR-Classifier. For the evaluation task, six topics have been selected to be identified: Culture, religion, economy, local news, international news and sports. An Arabic corpus has been used to achieve experiments.
Keywords : TR-Classifier
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Contributor : Kamel Smaïli <>
Submitted on : Wednesday, September 13, 2017 - 12:50:19 AM
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  • HAL Id : hal-01586533, version 1



Mourad Abbas, Kamel Smaïli, D Berkani. Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies. 3rd International Conference on Arabic Language Processing, May 2009, Rabat, Morocco. ⟨hal-01586533⟩



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