%0 Conference Paper %1 kluegl2012stacked %A Klügl, Peter %A Toepfer, Martin %A Lemmerich, Florian %A Hotho, Andreas %A Puppe, Frank %B Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM %C Vilamoura, Algarve, Portugal %D 2012 %E Carmona, Pedro Latorre %E Sánchez, J. Salvador %E Fred, Ana %I SciTePress %K 2012 conditional crf fields myown random stacked %P 240-248 %T Stacked Conditional Random Fields Exploiting Structural Consistencies %U http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2012-ICPRAM-StackedCRF.pdf %X Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.

@inproceedings{kluegl2012stacked, abstract = {Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, added-at = {2015-10-14T11:57:09.000+0200}, address = {Vilamoura, Algarve, Portugal}, author = {Klügl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, biburl = {https://www.bibsonomy.org/bibtex/27920d13d4fce68bb9a4947585083986e/kde-alumni}, booktitle = {Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM}, editor = {Carmona, Pedro Latorre and Sánchez, J. Salvador and Fred, Ana}, interhash = {74969e59c5637d192021e35bbd02bece}, intrahash = {7920d13d4fce68bb9a4947585083986e}, keywords = {2012 conditional crf fields myown random stacked}, pages = {240-248}, publisher = {SciTePress}, timestamp = {2015-10-14T11:57:09.000+0200}, title = {Stacked Conditional Random Fields Exploiting Structural Consistencies}, url = {http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2012-ICPRAM-StackedCRF.pdf}, year = 2012 }