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TildeCRF: Conditional Random Fields for Logical Sequences

  • Bernd Gutmann
  • Kristian Kersting
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Abstract

Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences over an alphabet of logical atoms. TildeCRF’s key idea is to use relational regression trees in Dietterich et al.’s gradient tree boosting approach. Thus, the CRF potential functions are represented as weighted sums of relational regression trees. Experiments show a significant improvement over established results achieved with hidden Markov models and Fisher kernels for logical sequences.

Keywords

Hide Markov Model Regression Tree Conditional Random Field Logical Sequence Ground Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bernd Gutmann
    • 1
  • Kristian Kersting
    • 1
  1. 1.Institute for Computer Science, Machine Learning LabUniversity of FreiburgFreiburgGermany

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