[Zhang et al.(2002)Zhang, Damereau, and Johnson] presented a generalised version of the Winnow algorithm. They observed that the original Winnow algorithm is only guaranteed to converge on linearly separable data. So, given the possibility that features for shallow parsing are not linearly separable, the authors modified Winnow such that it would converge, even for non-linearly separable features. They also showed that both versions of Winnow were robust to irrelevant features.
The authors used a very large set of features, including those derived from sources other than the training set. Winnow was found to be a strong performer for this task, giving the best results reported for a non-ensemble classifier in the CoNLL 2000 shared task. Clearly, the ability to exploit very large numbers of (potentially irrelevant) features is a crucial component of a successful shallow parsing system.