@InProceedings{gupta-schutze-andrassy:2016:COLING,
  author    = {Gupta, Pankaj  and  Sch\"{u}tze, Hinrich  and  Andrassy, Bernt},
  title     = {Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction},
  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     = {2537--2547},
  abstract  = {This paper proposes a novel context-aware joint entity and word-level relation
	extraction approach through semantic composition of words, introducing a Table
	Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the
	entity recognition and relation classification tasks to a table-filling problem
	and models their interdependencies. The proposed neural
	network architecture is capable of modeling multiple relation instances without
	knowing the corresponding relation arguments in a sentence. The experimental
	results show that a simple approach of piggybacking candidate entities to model
	the label dependencies from relations to entities improves performance.
	We present state-of-the-art results with improvements of 2.0% and 2.7% for
	entity recognition and relation classification, respectively on CoNLL04
	dataset.},
  url       = {http://aclweb.org/anthology/C16-1239}
}