---
res:
  bibo_abstract:
  - We consider Conditional random fields (CRFs) with pattern-based potentials defined
    on a chain. In this model the energy of a string (labeling) (Formula presented.)
    is the sum of terms over intervals [i, j] where each term is non-zero only if
    the substring (Formula presented.) equals a prespecified pattern w. 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 (Formula presented.), (Formula presented.) and (Formula presented.)
    where L is the combined length of input patterns, (Formula presented.) is the
    maximum length of a pattern, and D is the input alphabet. This improves on the
    previous algorithms of Ye et al. (NIPS, 2009) whose complexities are respectively
    (Formula presented.), (Formula presented.) and (Formula presented.), where (Formula
    presented.) 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Vladimir
      foaf_name: Kolmogorov, Vladimir
      foaf_surname: Kolmogorov
      foaf_workInfoHomepage: http://www.librecat.org/personId=3D50B0BA-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Rustem
      foaf_name: Takhanov, Rustem
      foaf_surname: Takhanov
      foaf_workInfoHomepage: http://www.librecat.org/personId=2CCAC26C-F248-11E8-B48F-1D18A9856A87
  bibo_doi: 10.1007/s00453-015-0017-7
  bibo_issue: '1'
  bibo_volume: 76
  dct_date: 2016^xs_gYear
  dct_identifier:
  - UT:000381149500002
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Inference algorithms for pattern-based CRFs on sequence data@
...