Uncertainty in Artificial Intelligence
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Mixture-of-Parents Maximum Entropy Markov Models
David Rosenberg, Dan Klein, Ben Taskar
We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies be- tween nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parents. We show how to efficiently compute the exact marginal posterior node distributions, regardless of the range of the dependencies. This enables us to model non-sequential correlations present within text documents, as well as between in- terconnected documents, such as hyperlinked web pages. We apply the MoP-MEMM to a named entity recognition task and a web page classification task. In each, our model shows significant improvement over the basic MEMM, and is competitive with other long- range sequence models that use approximate inference.
Pages: 318-325
PS Link:
PDF Link: /papers/07/p318-rosenberg.pdf
AUTHOR = "David Rosenberg and Dan Klein and Ben Taskar",
TITLE = "Mixture-of-Parents Maximum Entropy Markov Models",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "318--325"

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