@InProceedings{raj-sahu-anand:2017:CoNLL,
  author    = {Raj, Desh  and  SAHU, SUNIL  and  Anand, Ashish},
  title     = {Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {311--321},
  abstract  = {The task of relation classification in the biomedical domain is complex due to
	the presence of samples obtained from heterogeneous sources such as research
	articles, discharge summaries, or electronic health records. It is also a
	constraint for classifiers which employ manual feature engineering. In this
	paper, we propose a convolutional recurrent neural network (CRNN) architecture
	that combines RNNs and CNNs in sequence to solve this problem. The rationale
	behind our approach is that CNNs can effectively identify coarse-grained local
	features in a sentence, while RNNs are more suited for long-term dependencies.
	We compare our CRNN model with several baselines on two biomedical datasets,
	namely the i2b2-2010 clinical relation extraction challenge dataset, and the
	SemEval-2013 DDI extraction dataset. We also evaluate an attentive pooling
	technique and report its performance in comparison with the conventional max
	pooling method. Our results indicate that the proposed model achieves
	state-of-the-art performance on both datasets.},
  url       = {http://aclweb.org/anthology/K17-1032}
}