  We study the use of greedy feature selection methods for morphosyntactic
tagging under a number of different conditions. We compare a static ordering of
features to a dynamic ordering based on mutual information statistics, and we
apply the techniques to standalone taggers as well as joint systems for tagging
and parsing. Experiments on five languages show that feature selection can
result in more compact models as well as higher accuracy under all conditions,
but also that a dynamic ordering works better than a static ordering and that
joint systems benefit more than standalone taggers. We also show that the same
techniques can be used to select which morphosyntactic categories to predict in
order to maximize syntactic accuracy in a joint system. Our final results
represent a substantial improvement of the state of the art for several
languages, while at the same time reducing both the number of features and the
running time by up to 80% in some cases.
