@InProceedings{yu-jiang:2016:COLING,
  author    = {Yu, Jianfei  and  Jiang, Jing},
  title     = {Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network},
  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     = {2366--2377},
  abstract  = {Relation classification is the task of classifying the semantic relations
	between entity pairs in text. Observing that existing work has not fully
	explored using different representations for relation instances, especially in
	order to better handle the asymmetry of relation types, in this paper, we
	propose a neural network based method for relation classification that combines
	the raw sequence and the shortest dependency path representations of relation
	instances and uses mirror instances to perform pairwise relation
	classification. We evaluate our proposed models on the SemEval-2010 Task 8
	dataset. The empirical results show that with two additional features, our
	model achieves the state-of-the-art result of F1 score of 85.7.},
  url       = {http://aclweb.org/anthology/C16-1223}
}