  We propose and compare various sentence selection strategies for active
learning for the task of detecting mentions of entities. The best strategy
employs the sum of confidences of two statistical classifiers trained on
different views of the data. Our experimental results show that, compared to
the random selection strategy, this strategy reduces the amount of required
labeled training data by over 50% while achieving the same performance. The
effect is even more significant when only named mentions are considered: the
system achieves the same performance by using only 42% of the training data
required by the random selection strategy.
