For NLP, bootstrapping is a popular approach to semi-supervised learning due its relative simplicity coupled with reasonable performance (Abney, 2007).
a crucial limitation of bootstrapping, which is typically iterative, is that, as learning advances, the task often drifts semantically into a related but different space, e.g., from learning women names into learning flower names (McIntosh, 2010;Yangarber, 2003).