@InProceedings{xu-EtAl:2016:COLING1,
  author    = {Xu, Yan  and  Jia, Ran  and  Mou, Lili  and  Li, Ge  and  Chen, Yunchuan  and  Lu, Yangyang  and  Jin, Zhi},
  title     = {Improved relation classification by deep recurrent neural networks with data augmentation},
  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     = {1461--1470},
  abstract  = {Nowadays, neural networks play an important role in the task of relation
	classification. By designing different neural architectures, researchers have
	improved the performance to a large extent in comparison with traditional
	methods. However, existing neural networks for relation classification are
	usually of shallow architectures (e.g., one-layer convolutional neural networks
	or recurrent networks). They may fail to explore the potential representation
	space in different abstraction levels. In this paper, we propose deep recurrent
	neural networks (DRNNs) for relation classification to tackle this challenge.
	Further, we propose a data augmentation method by leveraging the directionality
	of relations. We evaluated our DRNNs on the SemEval-2010 Task~8, and achieve an
	F1-score of 86.1%, outperforming previous state-of-the-art recorded results.},
  url       = {http://aclweb.org/anthology/C16-1138}
}