@InProceedings{huang-EtAl:2017:EMNLP20172,
  author    = {Huang, Lifu  and  Sil, Avirup  and  Ji, Heng  and  Florian, Radu},
  title     = {Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2588--2597},
  abstract  = {Slot Filling (SF) aims to extract the values of certain types of attributes (or
	slots, such as person:cities\_of\_residence) for a given entity from a large
	collection of source documents. 
	In this paper we propose an effective DNN architecture for SF with the
	following new strategies: (1). Take a regularized dependency graph instead of a
	raw sentence as input to DNN, to compress the wide contexts between query and
	candidate filler; (2). Incorporate two attention mechanisms: local attention
	learned from query and candidate filler, and global attention learned from
	external knowledge bases, to guide the model to better select indicative
	contexts to determine slot type. Experiments show that this framework
	outperforms state-of-the-art on both relation extraction (16% absolute F-score
	gain) and slot filling validation for each individual system (up to 8.5%
	absolute F-score gain).},
  url       = {https://www.aclweb.org/anthology/D17-1274}
}