@InProceedings{shen-huang:2016:COLING,
  author    = {shen, yatian  and  Huang, Xuanjing},
  title     = {Attention-Based Convolutional Neural Network for Semantic 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     = {2526--2536},
  abstract  = {Nowadays, neural networks play an
	important role in the task of relation classification.
	In this paper,
	we propose a novel attention-based convolutional
	neural network architecture for this task. Our model makes full use of
	word embedding, part-of-speech tag embedding and position embedding
	information.
	Word level attention mechanism is able to
	better determine which parts of the sentence
	are most influential with respect to the two entities
	of interest.
	This architecture enables learning some important features from task-specific
	labeled
	data, forgoing the need for external
	knowledge such as explicit dependency structures.
	Experiments on the SemEval-2010 Task 8
	benchmark dataset show that our model
	achieves better performances than several state-of-the-art neural network
	models and can achieve a competitive performance just with minimal feature
	engineering.},
  url       = {http://aclweb.org/anthology/C16-1238}
}