  We use a Dynamic Bayesian Network to represent compactly a variety of
sublexical and contextual features relevant to Part-of-Speech (PoS) tagging.
The outcome is a flexible tagger (LegoTag) with state-of-the-art performance
(3.6% error on a benchmark corpus). We explore the effect of eliminating
redundancy and radically reducing the size of feature vocabularies. We find
that a small but linguistically motivated set of suffixes results in improved
cross-corpora generalization. We also show that a minimal lexicon limited to
function words is sufficient to ensure reasonable performance.
