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

This paper presents our new approach to construct a system of transformation rules for the Part-Of-Speech tagging task. Our tagging 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 rules. Experiments on 13 languages exhibit that our method is fast in terms of training time and tagging speed. Furthermore, our method is able to attain state-of-the-art accuracies for relatively isolating or analytic languages while obtaining competitive accuracy results on morphologically rich Indo-European languages.