@InProceedings{lu-EtAl:2016:COLING,
  author    = {Lu, Jing  and  Venugopal, Deepak  and  Gogate, Vibhav  and  Ng, Vincent},
  title     = {Joint Inference for Event Coreference Resolution},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3264--3275},
  abstract  = {Event coreference resolution is a challenging problem since it relies on
	several components of the information extraction pipeline that typically yield
	noisy outputs. We hypothesize that exploiting the inter-dependencies between
	these components can significantly improve the performance of an event
	coreference resolver, and subsequently propose a novel joint inference based
	event coreference resolver using Markov Logic Networks (MLNs). However, the
	rich features that are important for this task are typically very hard to
	explicitly encode as MLN formulas since they significantly increase the size of
	the MLN, thereby making joint inference and learning infeasible. To address
	this problem, we propose a novel solution where we implicitly encode rich
	features into our model by augmenting the MLN distribution with low dimensional
	unit clauses. Our approach achieves state-of-the-art results on two standard
	evaluation corpora.},
  url       = {http://aclweb.org/anthology/C16-1308}
}