Interestingly, the feature-update step has a dramatic influence in performance in both corpora: On one hand, the performance peak in a dataset with less linguistic variability (MSR-NLP) is reached early, and after iteration 100, where the feature update step occurs, Precision decreases, while Recall remains the same.
the numbers in the W00 dataset are fairly stable until iteration 100, where a significant improvement in both Precision and Recall is achieved.