  We study the performance of Arabic text classification combining various
techniques: (a) tfidf vs. dependency syntax, for feature selection and
weighting; (b) class association rules vs. support vector machines, for
classification. The Arabic text is used in two forms: rootified and lightly
stemmed. The results we obtain show that lightly stemmed text leads to better
performance than rootified text; that class association rules are better suited
for small feature sets obtained by dependency syntax constraints; and, finally,
that support vector machines are better suited for large feature sets based on
morphological feature selection criteria.
