Watan-2004
Watan-2004 corpus contains about 20000 articles talking about the six following topics "categories":  Culture, Religion, Economy, Local News, International News and sports.  In this corpus, punctuation has been omitted intentionally in order to make it useful for Language Modeling.
Multiple institutions
Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies 
topic classification
International Conference on Arabic Language Processing 
M. Abbas, K. Smaili, and D. Berkani
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. 
