Dialogue Act Tagging with Transformation-Based Learning
| dc.creator | Samuel, Ken | |
| dc.creator | Carberry, Sandra | |
| dc.creator | Vijay-Shanker, K. | |
| dc.date | 1998-06-08 | |
| dc.date.accessioned | 2026-07-07T02:36:15Z | |
| dc.date.available | 2026-07-07T02:36:15Z | |
| dc.description | 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. | |
| dc.description | 7 pages, no Postscript figures, uses colacl.sty and acl.bst | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9806006 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9806006 | |
| dc.identifier | Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL '98) | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/15850 | |
| dc.subject | Computation and Language | |
| dc.title | Dialogue Act Tagging with Transformation-Based Learning | |
| dc.type | text |