@article{kluegl2013exploiting, 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. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, 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.}, author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, interhash = {9ef3f543e4cc9e2b0ef078595f92013b}, intrahash = {fbaab25e96dd20d96ece9d7fefdc3b4f}, journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics}, pages = {111-125}, title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields}, volume = 30, year = 2013 } @article{atzmueller2013exploratory, author = {Atzmueller, Martin and Lemmerich, Florian}, interhash = {6e83bf4017fffe31a5632289d91c1b6d}, intrahash = {9f176520035c05191d77ebd53803b817}, journal = {International Journal of Web Science (Special Issue on Social Web Search and Mining)}, number = {1/2}, title = {{Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information}}, volume = 2, year = 2013 }