PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stacked Dependency Networks for Layout Document Structuring
Boris Chidlovskii and Loic Lecerf
Journal Universal Computer Science Volume 14, Number 18, pp. 2998-3010, 2008.

Abstract

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:5319
Deposited By:Boris Chidlovskii
Deposited On:24 March 2009