The price to pay for these speedups is that there may be collisions, so that different features are mapped to the same index, but this is often compensated by the fact that the lower time and memory requirements of the hash kernel enables the use of negative features, that is, features that are never seen in the training set but occur in erroneous hypotheses at training time and can therefore be helpful also at inference time.
the hash kernel often improves accuracy as well as efficiency compared to traditional techniques that only make use of features that occur in gold standard parses (Bohnet, 2010).