@InProceedings{jiang-EtAl:2016:COLING1,
  author    = {Jiang, Xiaotian  and  Wang, Quan  and  Li, Peng  and  Wang, Bin},
  title     = {Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks},
  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     = {1471--1480},
  abstract  = {Distant supervision is an efficient approach that automatically generates
	labeled data for relation extraction (RE). Traditional distantly supervised RE
	systems rely heavily on handcrafted features, and hence suffer from error
	propagation. Recently, a neural network architecture has been proposed to
	automatically extract features for relation classification. However, this
	approach follows the traditional expressed-at-least-once assumption, and fails
	to make full use of information across different sentences. Moreover, it
	ignores the fact that there can be multiple relations holding between the same
	entity pair. In this paper, we propose a multi-instance multi-label
	convolutional neural network for distantly supervised RE. It first relaxes the
	expressed-at-least-once assumption, and employs cross-sentence max-pooling so
	as to enable information sharing across different sentences. Then it handles
	overlapping relations by multi-label learning with a neural network classifier.
	Experimental results show that our approach performs significantly and
	consistently better than state-of-the-art methods.},
  url       = {http://aclweb.org/anthology/C16-1139}
}