While active learning has been shown to work for a number of tasks, the majority of active learning experiments in natural language processing have been conducted using very small seed corpora and sets of unlabeled examples.
we wish to explore situations where we have, or can afford, a nonnegligible sized training corpus (such as for part-of-speech tagging) and have access to very large amounts of unlabeled data.