Transformation-Based Learning in the Fast Lane
<|reference_start|>Transformation-Based Learning in the Fast Lane: Transformation-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.<|reference_end|>
@article{ngai2001transformation-based,
    title={Transformation-Based Learning in the Fast Lane},
    author={Grace Ngai, Radu Florian},
    journal={Proceedings of the Second Conference of the North American Chapter
  of the Association for Computational Linguistics, pages 40-47, Pittsburgh,
  PA, USA},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0107020},
    primaryClass={cs.CL}
}