A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-Of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-Of-Speech tagging task. Our approach is based on an incremental knowledge acquisition methodology where rules are stored in an exception-structure and new rules are only added to correct errors of existing rules; thus allowing systematic control of the interaction between the rules. Experiments 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 a state-of-the-art POS and morphological tagger.