@article{daume09searn,
  author =       {Hal {Daum\'e III} and John Langford and Daniel Marcu},
  title =        {Search-based Structured Prediction},
  year =         {2009},
  booktitle =    {Machine Learning Journal (MLJ)},
  abstract =     {
    We present Searn, an algorithm for integrating search and
    learning to solve complex structured prediction problems such
    as those that occur in natural language, speech, computational
    biology, and vision.  Searn is a meta-algorithm that transforms
    these complex problems into simple classification problems to which
    any binary classifier may be applied.  Unlike current algorithms for
    structured learning that require decomposition of both the loss
    function and the feature functions over the predicted structure,
    Searn is able to learn prediction functions for any loss
    function and any class of features.  Moreover, Searn comes
    with a strong, natural theoretical guarantee: good performance on the
    derived classification problems implies good performance on the
    structured prediction problem.
  },
  url = {http://pub.hal3.name/#daume06searn}
}