@InProceedings{karn-waltinger-schutze:2017:EACLshort,
  author    = {Karn, Sanjeev  and  Waltinger, Ulli  and  Sch\"{u}tze, Hinrich},
  title     = {End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification},
  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     = {752--758},
  abstract  = {We address fine-grained entity classification and
	propose a novel attention-based recurrent neural network
	(RNN) encoder-decoder that generates paths in the type hierarchy and can be
	trained end-to-end.  
	We show that our model performs better
	on fine-grained entity classification than prior work that relies on
	flat or local classifiers that do not directly model
	hierarchical structure.},
  url       = {http://www.aclweb.org/anthology/E17-2119}
}