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Stacked dependency networks for layout document structuring

Boris Chidlovskii, Loïc Lecerf
We address the problems of the structuring and annotation of layout-oriented documents. We model the annotation problems as the collective classification on graph-like strucutres with typed instances and links that capture the domain-specific knowledge. We use the relational dependency networks (RDNs) for the collective inference on the multi-typed graphs. We describe them a variant of RDNs where a stacked approximation replaces the Gibbs sampling in order to accelerate the inference. We report results of evaluation tests for both the Gibbs sampling and stacking inference on two document structuring examples
SIGIR Information Retrieval and Graphical Model Workshop, Amsterdam, The Netherlands, July 25 ,2007.