Hierarchical non-emitting Markov models

Authors:
Eric Sven Ristad;Robert G. Thomas
Affiliations:
Princeton University, Princeton, NJ;Princeton University, Princeton, NJ
Venue:
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Year:
1997

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Abstract

We describe a simple variant of the interpolated Markov model with non-emitting state transitions and prove that it is strictly more powerful than any Markov model. Empirical results demonstrate that the non-emitting model outperforms the interpolated model on the Brown corpus and on the Wall Street Journal under a wide range of experimental conditions. The non-emitting model is also much less prone to overtraining.