@InProceedings{nguyen-EtAl:2016:COLING,
  author    = {Nguyen, Thien Huu  and  Fauceglia, Nicolas  and  Rodriguez Muro, Mariano  and  Hassanzadeh, Oktie  and  Massimiliano Gliozzo, Alfio  and  Sadoghi, Mohammad},
  title     = {Joint Learning of Local and Global Features for Entity Linking via Neural Networks},
  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     = {2310--2320},
  abstract  = {Previous studies have highlighted the necessity for entity linking systems to
	capture the local entity-mention similarities and the global topical coherence.
	We introduce a novel framework based on convolutional neural networks and
	recurrent neural networks to simultaneously model the local and global features
	for entity linking. The proposed model benefits from the capacity of
	convolutional neural networks to induce the underlying representations for
	local contexts and the advantage of recurrent neural networks to adaptively
	compress variable length sequences of predictions for global constraints. Our
	evaluation on multiple datasets demonstrates the effectiveness of the model and
	yields the state-of-the-art performance on such datasets. In addition, we
	examine the entity linking systems on the domain adaptation setting that
	further demonstrates the cross-domain robustness of the proposed model.},
  url       = {http://aclweb.org/anthology/C16-1218}
}