An Investigation of Transformation-Based Learning in Discourse [article]

Ken Samuel, Sandra Carberry, and K. Vijay-Shanker (Department of Computer and Information Sciences, University of Delaware)
1998 arXiv   pre-print
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
more » ... ese 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.
arXiv:cmp-lg/9806007v1 fatcat:p4krwr3sgna4pinymq6vrou2hm