In these approaches, each observation corresponds to a particular word and each hidden state corresponds to a cluster.
using maximum likelihood training for such models does not achieve good results (Clark, 2003): maximum likelihood training tends to result in very ambiguous distributions for common words, in contradiction with the rather sparse word-tag distribution.