- We address the task of unsupervised POS tag- ging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the task of full morphological disambigua- tion in Hebrew achieving an error reduction of 25% over a strong uniform distribution base- line. We also test the same method on the stan- dard WSJ unsupervised POS tagging task and obtain results competitive with recent state-of- the-art methods, while using simple and effi- cient learning methods.