Uncertainty in Artificial Intelligence
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Reduction of Maximum Entropy Models to Hidden Markov Models
Joshua Goodman
We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or other characteristics. The models can be trained using the forward-backward algorithm. While the results are primarily of theoretical interest, unifying apparently unrelated concepts, we also give experimental results for a maxent model with a hidden variable on a word disambiguation task; the model outperforms standard techniques.
Pages: 179-186
PS Link:
PDF Link: /papers/02/p179-goodman.pdf
AUTHOR = "Joshua Goodman ",
TITLE = "Reduction of Maximum Entropy Models to Hidden Markov Models",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "179--186"

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