1998/06/08 by Ken Samuel, Sandra Carberry, K. Vijay-Shanker
Computer Science · #cmp-lg #cs.CL
published as Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL '98) · 7 pages, no Postscript figures, uses colacl.sty and acl.bst
arxiv created 1998/06/08 · arxiv updated 2009/11/30
For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.