Paper: Unified Expectation Maximization

ACL ID N12-1087
Title Unified Expectation Maximization
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing al- gorithms like standard EM and hard EM, con- strained versions of EM such as Constraint- Driven Learning (Chang et al., 2007) and Pos- terior Regularization (Ganchev et al., 2010), along with a range of new EM algorithms. For the constrained inference step in UEM we present an efficient dual projected gradient as- cent algorithm which generalizes several dual decomposition and Lagrange relaxation algo- rithms popularized recently in the NLP litera- ture (Ganchev et al., 2008; Koo et al., 2010; Rush and Collins, 2011). UEM is as efficient and easy to implement as standard EM. F...