@InProceedings{yu-huang-ji:2017:I17-1,
  author    = {Yu, Dian  and  Huang, Lifu  and  Ji, Heng},
  title     = {Open Relation Extraction and Grounding},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {854--864},
  abstract  = {Previous open Relation Extraction (open RE) approaches mainly rely on
	linguistic patterns and constraints to extract important relational triples
	from large-scale corpora. However, they lack of abilities to cover diverse
	relation expressions or measure the relative importance of candidate triples
	within a sentence. It is also challenging to name the relation type of a
	relational triple merely based on context words, which could limit the
	usefulness of open RE in downstream applications. We propose a novel
	importance-based open RE approach by exploiting the global structure of a
	dependency tree to extract salient triples. We design an unsupervised relation
	type naming method by grounding relational triples to a large-scale Knowledge
	Base (KB) schema, leveraging KB triples and weighted context words associated
	with relational triples. Experiments on the English Slot Filling 2013 dataset
	demonstrate that our approach achieves 8.1% higher F-score over
	state-of-the-art open RE methods.},
  url       = {http://www.aclweb.org/anthology/I17-1086}
}