@unpublished{daume06searn-practice,
  author =       {Hal {Daum\'e III} and John Langford and Daniel Marcu},
  title =        {Searn in Practice},
  year =         {2006},
  abstract = {
    We recently introduced an algorithm, Searn, for solving hard
    structured prediction problems.  This algorithm
    enjoys many nice properties: efficiency, wide applicability,
    theoretical justification and simplicity.  However, under a desire to
    fit a lot of information into the original paper,
    it may not be so clear how simple the technique is.  This report is
    designed to showcase how Searn can be applied to a wide variety of
    techniques and what really goes on behind the scenes.  We will
    make use of three example problems, ranging from simple to complex.
    These are: (1) sequence labeling, (2) parsing and (3) machine
    translation.  (These were chosen to be as widely understandable,
    especially in the NLP community, as possible.)  In the end, we will
    come back to discuss Searn for general problems.
  },
  keywords = {nlp ml sp},
  tagline = {We recently introduced an algorithm, Searn, for solving hard structured prediction problems. This report is designed to showcase how Searn can be applied to a wide variety of techniques and what really goes on behind the scenes. We show how to apply Searn to three common NLP problems: (1) sequence labeling, (2) parsing and (3) machine translation.},
  url = {http://pub.hal3.name/#daume06searn-practice}
}