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.
In REF , the authors lifted CRFs to the relational case by representing the potential functions as a sum of relational regression trees learnt by a relational regression tree learner.
TildeCRF: Conditional random fields for logical sequences
In Proceedings of the 15th European Conference on Machine Learning (ECML-06
Computer Science