@InProceedings{daume09mrtf,
  author =       {Hal {Daum\'e III}},
  title =        {Markov Random Topic Fields},
  booktitle =    {Association for Computational Linguistics (ACL)},
  year =         {2009},
  address =      {Singapore},
  abstract =     {
    Most approaches to topic modeling assume an independence between
    documents that is frequently violated.  We present an topic model
    that makes use of one or more user-specified graphs describing
    relationships between documents.  These graph are encoded in the
    form of a Markov random field over topics and serve to encourage
    related documents to have similar topic structures.  Experiments on
    show upwards of a $10\%$ improvement in modeling performance.
  },
  keywords = {nlp bayes ml},
  tagline = {We show how to integrate topic models in an undirected graph for topic mining in -- for instance -- scientific publications. We explore several different model parameterizations.},
  url = {http://pub.hal3.name/#daume09mrtf}
}