Stacked Dependency Networks for Layout Document Structuring

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 de- pendency networks (RDNs) for the collective inference on the multi-typed graphs. We then de- scribe 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.