Stacked Dependency Networks for Layout Document Structuring

dc.creatorChidlovskii,Boris
dc.creatorLecerf,Loïc
dc.date2008
dc.date.accessioned2024-02-06T12:57:11Z
dc.date.available2024-02-06T12:57:11Z
dc.descriptionWe 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 describe a variant of RDNs where a stacked approximation replaces the Gibbs sampling in orderto accelerate the inference. We report results of evaluation tests for both the Gibbs sampling and stacking inference on two document structuring examples.
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-014-18-2998
dc.identifierhttps://lib.jucs.org/article/29210/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/9934
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 14(18): 2998-3010
dc.subjectdocument structuring
dc.subjectdependency networks
dc.subjectstacking
dc.titleStacked Dependency Networks for Layout Document Structuring
dc.typeResearch Article
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