  We present an algorithm that automatically learns context constraints using
statistical decision trees. We then use the acquired constraints in a flexible
POS tagger. The tagger is able to use information of any degree: n-grams,
automatically learned context constraints, linguistically motivated manually
written constraints, etc. The sources and kinds of constraints are
unrestricted, and the language model can be easily extended, improving the
results. The tagger has been tested and evaluated on the WSJ corpus.
