@InProceedings{yaghoobzadeh-adel-schutze:2017:EACLlong,
  author    = {Yaghoobzadeh, Yadollah  and  Adel, Heike  and  Sch\"{u}tze, Hinrich},
  title     = {Noise Mitigation for Neural Entity Typing and Relation Extraction},
  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     = {1183--1194},
  abstract  = {In this paper, we address two different types of noise in information
	extraction models: noise from distant supervision and noise from pipeline input
	features. Our
	target tasks are entity typing and relation extraction. For the first noise
	type, we introduce multi-instance multi-label learning algorithms using neural
	network models, and apply them to fine-grained entity typing for the first
	time. Our model outperforms the state-of-the-art supervised approach which uses
	global embeddings of entities. For the second noise type, we propose ways to
	improve the integration of noisy entity type predictions into relation
	extraction. Our experiments show that  probabilistic predictions are more
	robust than discrete predictions and that joint training of the two tasks
	performs best.},
  url       = {http://www.aclweb.org/anthology/E17-1111}
}