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

TL;DR

This paper introduces a new incremental, exception-based transformation rule system for POS tagging that is fast and achieves competitive accuracy across multiple languages.

Contribution

It presents a novel ripple down rules method for POS tagging that improves control and efficiency over previous transformation-based approaches.

Findings

Fast training and tagging speeds across 13 languages

Achieves accuracy comparable to state-of-the-art POS taggers

Effective in handling multiple languages and morphological variations

Abstract

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.