Paper: Online EM for Unsupervised Models

ACL ID N09-1069
Title Online EM for Unsupervised Models
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

The (batch) EM algorithm plays an important role in unsupervised induction, but it some- times suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find bet- ter solutions than those found by batch EM. We support these findings on four unsuper- vised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.