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TildeCRF: Conditional Random Fields for Logical Sequences

Bernd Gutmann; Kristian Kersting
In: Johannes Fürnkranz; Tobias Scheffer; Myra Spiliopoulou (Hrsg.). Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Proceedings. European Conference on Machine Learning (ECML-2006), September 18-22, Berlin, Germany, Pages 174-185, Lecture Notes in Computer Science (LNCS), Vol. 4212, Springer, 2006.


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.

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