SNoW is proven to perform very well in high dimensional NLP problems, where both the training examples and the target function reside very sparsely in the feature space (Roth, 1998), e.g: context-sensitive spelling correction, POS tagging, PP-attachment disambiguation, etc.
the sign of the prediction of many of these common rules was somewhat contradictory between corpora.