Name: Watan-2004
Description: 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.
Provider: Multiple institutions
Paper Title: Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies 
Tasks: topic classification
Venue Name: International Conference on Arabic Language Processing 
Authors: M. Abbas, K. Smaili, and D. Berkani
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. 
  