  Corpus-based methods for natural language processing often use supervised
training, requiring expensive manual annotation of training corpora. This paper
investigates methods for reducing annotation cost by {\it sample selection}. In
this approach, during training the learning program examines many unlabeled
examples and selects for labeling (annotation) only those that are most
informative at each stage. This avoids redundantly annotating examples that
contribute little new information. This paper extends our previous work on {\it
committee-based sample selection} for probabilistic classifiers. We describe a
family of methods for committee-based sample selection, and report experimental
results for the task of stochastic part-of-speech tagging. We find that all
variants achieve a significant reduction in annotation cost, though their
computational efficiency differs. In particular, the simplest method, which has
no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger.
