  In this paper, we address the problems of Arabic Text Classification and
stemming using Transducers and Rational Kernels. We introduce a new stemming
technique based on the use of Arabic patterns (Pattern Based Stemmer). Patterns
are modelled using transducers and stemming is done without depending on any
dictionary. Using transducers for stemming, documents are transformed into
finite state transducers. This document representation allows us to use and
explore rational kernels as a framework for Arabic Text Classification.
Stemming experiments are conducted on three word collections and classification
experiments are done on the Saudi Press Agency dataset. Results show that our
approach, when compared with other approaches, is promising specially in terms
of Accuracy, Recall and F1.
