@InProceedings{haponchyk-moschitti:2017:EACLshort,
  author    = {Haponchyk, Iryna  and  Moschitti, Alessandro},
  title     = {A Practical Perspective on Latent Structured Prediction for Coreference Resolution},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {143--149},
  abstract  = {Latent structured prediction theory proposes powerful methods such as Latent
	SVM\^{}{struct} (LSSVM), which can potentially be very appealing for coreference
	resolution (CR). In contrast, only small work is available, mainly targeting
	the latent structured perceptron (LSP).
	In this paper, we carried out a practical study comparing for the first time
	online learning with LSSVM. We analyze the intricacies that may have made
	initial attempts to use LSSVM fail, i.e., a huge training time and much lower
	accuracy produced by Kruskal's spanning tree algorithm. In this respect, we
	also propose a new effective feature selection approach for improving system
	efficiency. The results show that LSP, if correctly parameterized, produces the
	same performance as LSSVM, being much more efficient.},
  url       = {http://www.aclweb.org/anthology/E17-2023}
}