An Investigation of Transformation-Based Learning in Discourse
<|reference_start|>An Investigation of Transformation-Based Learning in Discourse: This paper presents results from the first attempt to apply Transformation-Based Learning to a discourse-level Natural Language Processing task. To address two limitations of the standard algorithm, we developed a Monte Carlo version of Transformation-Based Learning to make the method tractable for a wider range of problems without degradation in accuracy, and we devised a committee method for assigning confidence measures to tags produced by Transformation-Based Learning. The paper describes these advances, presents experimental evidence that Transformation-Based Learning is as effective as alternative approaches (such as Decision Trees and N-Grams) for a discourse task called Dialogue Act Tagging, and argues that Transformation-Based Learning has desirable features that make it particularly appealing for the Dialogue Act Tagging task.<|reference_end|>
@article{samuel1998an,
    title={An Investigation of Transformation-Based Learning in Discourse},
    author={Ken Samuel, Sandra Carberry, and K. Vijay-Shanker (Department of
  Computer and Information Sciences, University of Delaware)},
    journal={Machine Learning: Proceedings of the 15th International Conference},
    year={1998},
    archivePrefix={arXiv},
    eprint={cmp-lg/9806007},
    primaryClass={cmp-lg cs.CL}
}