Spectral Learning of Refinement HMMs

K. Stratos, A. M. Rush, S. B. Cohen, M. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We derive a spectral algorithm for learning 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 spectral methods, it avoids the problem of local optima and provides a consistent estimate 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.
Original languageEnglish
Title of host publicationProceedings of CoNLL
Pages56-64
Number of pages9
Publication statusPublished - 2013

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