IndexedFeatureMap.__repr__()
- refactor train() into train() and decode(), which supports steps
common to the training and test scenarios
- rename command line flags useBIO -> bio, useCostAug -> costAug, and
pass their values through to train() and decode()
- support serialization of the full DiscriminativeTagger instance:
saveModel(), loadModel()
- always pass the data as arguments, don't store it as a member of
DiscriminativeTagger
- when creating features from the training data, store tag frequencies
- remove duplicate legalTagBigram() implementation
memoize legalTagBigram() for a huge speedup in --bio mode
remove debugging print of weights
debugging print comment