@InProceedings{zeng-EtAl:2017:EMNLP2017,
  author    = {Zeng, Wenyuan  and  Lin, Yankai  and  Liu, Zhiyuan  and  Sun, Maosong},
  title     = {Incorporating Relation Paths in Neural Relation Extraction},
  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     = {1768--1777},
  abstract  = {Distantly supervised relation extraction has been widely used to find novel
	relational facts from plain text. To predict the relation between a pair of two
	target entities, existing methods solely rely on those direct sentences
	containing both entities. In fact, there are also many sentences containing
	only one of the target entities, which also provide rich useful information but
	not yet employed by relation extraction. To address this issue, we build
	inference chains between two target entities via intermediate entities, and
	propose a path-based neural relation extraction model to encode the relational
	semantics from both direct sentences and inference chains. Experimental results
	on real-world datasets show that, our model can make full use of those
	sentences containing only one target entity, and achieves significant and
	consistent improvements on relation extraction as compared with strong
	baselines. The source code of this paper can be obtained from https://
	github.com/thunlp/PathNRE.
	Author{4}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1186}
}