Paper: Spectral Learning of Refinement HMMs

ACL ID W13-3507
Title Spectral Learning of Refinement HMMs
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013

We derive a spectral algorithm for learn- ing the parameters of a refinement HMM. This method is simple, efficient, and can be applied to a wide range of supervised sequence labeling tasks. Like other spec- tral methods, it avoids the problem of lo- cal optima and provides a consistent esti- mate of the parameters. Our experiments on a phoneme recognition task show that when equipped with informative feature functions, it performs significantly better than a supervised HMM and competitively with EM.