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|a eng
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044 |
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|b المغرب
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100 |
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|9 4666
|a Abbas, Mourad
|e Author
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245 |
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|a Comparing TR-Classifier and KNN by Using reduced sizes of Vocabularies
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260 |
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|b معهد الدراسات والأبحاث للتعريب
|c 2009
|g مايو
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300 |
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|a 1 - 4
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336 |
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|a بحوث المؤتمرات
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520 |
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|b 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.
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653 |
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|a حوسبة اللغة العربية
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653 |
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|a المعالجة الآلية للغة العربية
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700 |
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|9 48570
|a Smaili, Kamel
|e Co-Author
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700 |
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|9 23776
|a Berkani, D.
|e Co-Author
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773 |
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|c 026
|d الرباط
|i معهد الدراسات والأبحاث للتعريب
|l 000
|o 6865
|s وقائع الندوة الثالثة الدولية حول المعالجة الآلية للغة العربية CITALA'09
|v 000
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856 |
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|u 6865-000-000-026.pdf
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930 |
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|d y
|p y
|q y
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995 |
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|a AraBase
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999 |
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|c 589857
|d 589857
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