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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Communication dans un congrès
3rd International Conference on Arabic Language Processing, May 2009, Rabat, Morocco
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https://hal.archives-ouvertes.fr/hal-01586533
Contributeur : Kamel Smaïli <>
Soumis le : mercredi 13 septembre 2017 - 00:50:19
Dernière modification le : jeudi 14 septembre 2017 - 01:09:13

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Mourad Abbas, Kamel Smaili, 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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