@InProceedings{xiao-liu:2016:COLING,
  author    = {Xiao, Minguang  and  Liu, Cong},
  title     = {Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention},
  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     = {1254--1263},
  abstract  = {Semantic relation classification remains a challenge in natural language
	processing. In this paper, we introduce a hierarchical recurrent neural network
	that is capable of extracting information from raw sentences for relation
	classification. Our model has several distinctive features: (1) Each sentence
	is divided into three context subsequences according to two annotated nominals,
	which allows the model to encode each context subsequence independently so as
	to selectively focus as on the important context information; (2) The
	hierarchical model consists of two recurrent neural networks (RNNs): the first
	one learns context representations of the three context subsequences
	respectively, and the second one computes semantic composition of these three
	representations and produces a sentence representation for the relationship
	classification of the two nominals. (3) The attention mechanism is adopted in
	both RNNs to encourage the model to concentrate on the important information
	when learning the sentence representations. Experimental results on the
	SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the
	state-of-the-art without using any hand-crafted features.},
  url       = {http://aclweb.org/anthology/C16-1119}
}