Transformation-Based Learning in the Fast Lane

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dc.creatorNgai, Grace
dc.creatorFlorian, Radu
dc.date2001-07-17
dc.date.accessioned2026-07-07T03:17:21Z
dc.date.available2026-07-07T03:17:21Z
dc.descriptionTransformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and does not overtrain easily. However, it does have a serious drawback: the training time is often intorelably long, especially on the large corpora which are often used in NLP. In this paper, we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacrificing performance. The paper compares and contrasts the training time needed and performance achieved by our modified learner with two other systems: a standard transformation-based learner, and the ICA system \cite{hepple00:tbl}. The results of these experiments show that our system is able to achieve a significant improvement in training time while still achieving the same performance as a standard transformation-based learner. This is a valuable contribution to systems and algorithms which utilize transformation-based learning at any part of the execution.
dc.description8 pages, 2 figures, presented at NAACL 2001
dc.identifierhttps://arxiv.org/abs/cs/0107020
dc.identifierhttp://arxiv.org/abs/cs/0107020
dc.identifierProceedings of the Second Conference of the North American Chapter of the Association for Computational Linguistics, pages 40-47, Pittsburgh, PA, USA
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30692
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleTransformation-Based Learning in the Fast Lane
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