@InProceedings{gashteovski-gemulla-delcorro:2017:EMNLP2017,
  author    = {Gashteovski, Kiril  and  Gemulla, Rainer  and  Del Corro, Luciano},
  title     = {MinIE: Minimizing Facts in Open Information 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     = {2630--2640},
  abstract  = {The goal of Open Information Extraction (OIE) is to extract surface relations
	and their arguments from natural-language text in an unsupervised,
	domain-independent manner. In this paper, we propose MinIE, an OIE system that
	aims to provide useful, compact extractions with high precision and
	recall. MinIE approaches these goals by (1) representing information about
	polarity, modality, attribution, and quantities with semantic annotations
	instead of in the actual extraction, and (2) identifying and removing parts
	that are considered overly specific. We conducted an experimental study with
	several real-world datasets and found that MinIE achieves competitive or
	higher precision and recall than most prior systems, while at the same time
	producing shorter, semantically enriched extractions.},
  url       = {https://www.aclweb.org/anthology/D17-1278}
}