Supervised learning exploits manually annotated data, but must make do with a relatively small amount of training text because manual annotations are expensive.
seed-based bootstrapping exploits a small number of human-provided seeds, but needs a larger set of (unannotated) texts for training because the seeds produce relatively sparse annotations of the texts.