TY  - CONF
AU  - Klügl, Peter
AU  - Toepfer, Martin
AU  - Lemmerich, Florian
AU  - Hotho, Andreas
AU  - Puppe, Frank
A2  - Carmona, Pedro Latorre
A2  - Sánchez, J. Salvador
A2  - Fred, Ana
T1  - Stacked Conditional Random Fields Exploiting Structural Consistencies
T2  - Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM
PB  - SciTePress
CY  - Vilamoura, Algarve, Portugal
PY  - 2012/
M2  - 
VL  - 
IS  - 
SP  - 240
EP  - 248
UR  - http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2012-ICPRAM-StackedCRF.pdf
M3  - 
KW  - 2012
KW  - conditional
KW  - crf
KW  - fields
KW  - myown
KW  - random
KW  - stacked
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 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.
ER  -

TY  - CONF
AU  - Toepfer, Martin
AU  - Kluegl, Peter
AU  - Hotho, Andreas
AU  - Puppe, Frank
A2  - 
T1  - Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions
T2  - Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation
PB  - 
CY  - 
PY  - 2011/
M2  - 
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2011-LWA-SkYp.pdf
M3  - 
KW  - 2011
KW  - chain
KW  - conditional
KW  - myown
KW  - references
KW  - segmentation
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - GEN
AU  - Sutton, Charles
AU  - McCallum, Andrew
A2  - 
T1  - An Introduction to Conditional Random Fields
JO  - 
PB  - 
AD  - 
PY  - 2010/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/1011.4088
M3  - 
KW  - condi
KW  - conditional
KW  - crf
KW  - fields
KW  - introduction
KW  - random
L1  - 
N1  - An Introduction to Conditional Random Fields
N1  - 
AB  -   Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields. 
ER  -