  In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; thus allowing systematic control of the interaction between the
rules. Experimental results on 13 languages show that our approach is fast in
terms of training time and tagging speed. Furthermore, our approach obtains
very competitive accuracy in comparison to state-of-the-art POS and
morphological taggers.
