In NLP, unsupervised learning typically implies optimization of a "bumpy" objective function riddled with local maxima.
one exception is IBM Model 1 (Brown et al., 1993) for word alignment, which is the only model commonly used for unsupervised learning in NLP that has a concave loglikelihood function.