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Inference algorithms for pattern-based CRFs on sequence data

Rustem Takhanov, Vladimir Kolmogorov
;
JMLR W&CP 28 (3) : 145–153, 2013

Abstract

We consider Conditional Random Fields (CRFs) with pattern-based potentials defined on a chain. In this model the energy of a string (labeling) \(x_1\ldots x_n\) is the sum of terms over intervals \([i,j]\) where each term is non-zero only if the substring \(x_i\ldots x_j\) equals a prespecified pattern \(\alpha\). Such CRFs can be naturally applied to many sequence tagging problems. We present efficient algorithms for the three standard inference tasks in a CRF, namely computing (i) the partition function, (ii) marginals, and (iii) computing the MAP. Their complexities are respectively \(O(n L)\), \(O(n L \ell_{\max})\) and \(O(n L \min\{|D|,\log (\ell_{\max} + 1)\})\) where \(L\) is the combined length of input patterns, \(\ell_{\max}\) is the maximum length of a pattern, and \(D\) is the input alphabet. This improves on the previous algorithms of  whose complexities are respectively \(O(n L |D|)\), \(O\left(n |\Gamma| L^2 \ell_{\max}^2\right)\) and \(O(n L |D|)\), where \(|\Gamma|\) is the number of input patterns. In addition, we give an efficient algorithm for sampling, and revisit the case of MAP with non-positive weights. Finally, we apply pattern-based CRFs to the problem of the protein dihedral angles prediction.

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