With the plenitude of unlabeled natural language data at hand, semi-supervised learning can significantly reduce the need for labeled data with only limited sacrifice in performance.
a bootstrapping algorithm chooses the unlabeled instances with the highest probability of being correctly labeled and use them to augment labeled training data iteratively.