Abstract
The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better 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. © 2009 Association for Computational Linguistics.
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CITATION STYLE
Liang, P., & Klein, D. (2009). Online EM for unsupervised models. In NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 611–619). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1620754.1620843
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