This POS tagger deterministically tags tokens. However, if it ever sees multiple tags for the same token, it will forget the tag it has learned. This is useful for creating taggers that have very high precision, but very low recall.
Unambiguous taggers are also useful when defined with a non-deterministic backoff tagger, such as an NLP.POS.AveragedPerceptronTagger, since the high-confidence tags will be applied first, followed by the more non-deterministic results of the backoff tagger.
Create an unambiguous tagger, using the supplied
Map as a
source of tags.