  Most recent research in trainable part of speech taggers has explored
stochastic tagging. While these taggers obtain high accuracy, linguistic
information is captured indirectly, typically in tens of thousands of lexical
and contextual probabilities. In [Brill92], a trainable rule-based tagger was
described that obtained performance comparable to that of stochastic taggers,
but captured relevant linguistic information in a small number of simple
non-stochastic rules. In this paper, we describe a number of extensions to this
rule-based tagger. First, we describe a method for expressing lexical relations
in tagging that are not captured by stochastic taggers. Next, we show a
rule-based approach to tagging unknown words. Finally, we show how the tagger
can be extended into a k-best tagger, where multiple tags can be assigned to
words in some cases of uncertainty.
