An Investigation of Transformation-Based Learning in Discourse
| dc.creator | Samuel, Ken | |
| dc.creator | Carberry, Sandra | |
| dc.creator | Vijay-Shanker, K. | |
| dc.date | 1998-06-09 | |
| dc.date.accessioned | 2026-07-07T02:36:15Z | |
| dc.date.available | 2026-07-07T02:36:15Z | |
| dc.description | 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. | |
| dc.description | 9 pages, 3 Postscript figure, uses ml98.sty | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9806007 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9806007 | |
| dc.identifier | Machine Learning: Proceedings of the 15th International Conference | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/15851 | |
| dc.subject | Computation and Language | |
| dc.title | An Investigation of Transformation-Based Learning in Discourse | |
| dc.type | text |