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Stacked Dependency Networks for Layout Document Structuring

Boris Chidlovskii, Loïc Lecerf
We address the problems of structuring and annotation of layout-oriented documents. We model the annotation problems as the collective classification on graph-like structures 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 then describe 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.
Appear in a Special issue on Document Engineering, in the Journal of Universal Computer Science, Vol 18, issue 14, pages 2998-3010.
Full paper available on JUCS website