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

arXiv: https://arxiv.org/abs/1412.4021 · HTML: https://mlsanity.com/paper/1412.4021/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2014.

## Built on by (most cited first, 9 of 9)

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- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)
- [LOCORE: Image Re-ranking with Long-Context Sequence Modeling](https://mlsanity.com/paper/2503.21772/) (2025)
- [Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://mlsanity.com/paper/2301.12642/) (2023)
- [Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words](https://mlsanity.com/paper/2104.01297/) (2021)
- [An Experimental Investigation of Part-Of-Speech Taggers for Vietnamese](https://mlsanity.com/paper/2206.06992/) (2022)

## Abstract (arXiv)

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.