EM Can Find Pretty Good HMM POS-Taggers (When Given a Good Start)

摘要

We address the task of unsupervised POS tagging. 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(tw). We test the method on the task of full morphological disambiguation in Hebrew achieving an error reduction of 25% over a strong uniform distribution baseline. We also test the same method on the standard WSJ unsupervised POS tagging task and obtain results competitive with recent state-of-the-art methods, while using simple and efficient learning methods.