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An Investigation of Transformation-Based Learning in Discourse

Ken Samuel·Sandra Carberry·K. Vijay-Shanker
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Abstract

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.

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Ken Samuel, Sandra Carberry, K. Vijay-Shanker. 1998-06-09. An Investigation of Transformation-Based Learning in Discourse. https://arxiv.org/abs/cmp-lg/9806007

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