Tractable Learning and Inference with Higher-Order Representations. Culotta, A. & McCallum, A. In ICML Workshop on Open Problems in Statistical Relational Learning (ICML WS), 2006.
Tractable Learning and Inference with Higher-Order Representations [pdf]Paper  bibtex   
@inproceedings{DBLP:conf/icml_ws/Culotta06,
  author    = {Aron Culotta and Andrew McCallum},
  title     = {Tractable Learning and Inference with Higher-Order Representations},
  booktitle = {ICML Workshop on Open Problems in Statistical Relational Learning (ICML WS)},
  year      = {2006},
  url       = {https://people.cs.umass.edu/~mccallum/papers/tractable-icmlws06.pdf},
  sum       = {When working with CRFs having features based on first-order logic, the "unrolled" graphical model would be far to large to fully instantiate. This paper describes a method leveraging MCMC to perform inference and learning while only partially instantiating the model. Positive results on entity resolution (of research papr authors) are described.},
}
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