We present a method that exploits unannotated corpora for compensating the paucity of annotated training data on the chunking and tagging tasks. It collects and compresses feature frequencies from a large unannotated corpus for use by linear classifiers. Experiments on two tasks show that it consistently produces significant performance improvements.
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