@InProceedings{ganea-hofmann:2017:EMNLP2017,
  author    = {Ganea, Octavian-Eugen  and  Hofmann, Thomas},
  title     = {Deep Joint Entity Disambiguation with Local Neural Attention},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2619--2629},
  abstract  = {We propose a novel deep learning model for joint document-level entity
	disambiguation, which leverages learned neural representations. Key components
	are entity embeddings, a neural attention mechanism over local context windows,
	and a differentiable joint inference stage for disambiguation. Our approach
	thereby combines benefits of deep learning with more traditional approaches
	such as graphical models and probabilistic mention-entity maps. Extensive
	experiments show that we are able to obtain competitive or state-of-the-art
	accuracy at moderate computational costs.},
  url       = {https://www.aclweb.org/anthology/D17-1277}
}