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 language | English |
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Title of host publication | Proceedings of CoNLL |
Pages | 56-64 |
Number of pages | 9 |
Publication status | Published - 2013 |