1998/06/09 by Ken Samuel, Sandra Carberry, K. Vijay-Shanker
Computer Science · #cmp-lg #cs.CL
published as Machine Learning: Proceedings of the 15th International Conference · 9 pages, 3 Postscript figure, uses ml98.sty
arxiv created 1998/06/09 · arxiv updated 2009/11/30
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.