Many machine learning methods have recently been applied to natural language processing
tasks. Among them, the Winnow algorithm has been argued to be particularly suitable for NLP problems, due to its robustness to irrelevant features. However in theory, Winnow may not converge for non-separable data. To remedy this problem, a modification called regularized Winnow has
been proposed. In this paper, we apply this new method to text chunking. We show that
this method achieves state of the art performance with significantly less computation
than previous approaches.
By: Tong Zhang
Published in: RC22011 in 2001
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