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Exploiting Structural Consistencies with Stacked Conditional Random Fields

  • Peter KlueglEmail author
  • Martin Toepfer
  • Florian Lemmerich
  • Andreas Hotho
  • Frank Puppe
Conference paper
  • 2.5k Downloads
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 30)

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.

Keywords

Collective information extraction Crf Stacked graphical models Structural consistencies Rule learning 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Peter Kluegl
    • 1
    • 2
    Email author
  • Martin Toepfer
    • 1
  • Florian Lemmerich
    • 1
  • Andreas Hotho
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer Science VIUniversity of WuerzburgWuerzburgGermany
  2. 2.Comprehensive Heart Failure CenterUniversity of WuerzburgWuerzburgGermany

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