@InProceedings{pawar-bhattacharyya-palshikar:2017:EACLlong,
  author    = {Pawar, Sachin  and  Bhattacharyya, Pushpak  and  Palshikar, Girish},
  title     = {End-to-end Relation Extraction using Neural Networks and Markov Logic Networks},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {818--827},
  abstract  = {End-to-end relation extraction refers to identifying boundaries of entity
	mentions, entity types of these mentions and appropriate semantic relation for
	each pair of mentions. Traditionally, separate predictive models were trained
	for each of these tasks and were used in a ``pipeline'' fashion where output
	of one model is fed as input to another. But it was observed that addressing
	some of these tasks jointly results in better performance. We propose a single,
	joint neural network based model to carry out all the three tasks of boundary
	identification, entity type classification and relation type classification.
	This model is referred to as ``All Word Pairs'' model (AWP-NN) as it assigns
	an appropriate label to each word pair in a given sentence for performing
	end-to-end relation extraction. We also propose to refine output of the AWP-NN
	model by using inference in Markov Logic Networks (MLN) so that additional
	domain knowledge can be effectively incorporated. We demonstrate effectiveness
	of our approach by achieving better end-to-end relation extraction performance
	than all 4 previous joint modelling approaches, on the standard dataset of ACE
	2004.},
  url       = {http://www.aclweb.org/anthology/E17-1077}
}