Mixture-of-Parents Maximum Entropy Markov Models
This addresses the challenge of modeling non-sequential correlations in text and web data for applications like information extraction, though it is an incremental extension of existing MEMMs.
The paper tackles the problem of incorporating long-range dependencies in Markov models by proposing the mixture-of-parents maximum entropy Markov model (MoP-MEMM), which allows tractable exact inference and shows significant improvement over basic MEMMs in named entity recognition and web page classification tasks.
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 between 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 interconnected 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.