@InProceedings{saha-pal-mausam:2017:Short,
  author    = {Saha, Swarnadeep  and  Pal, Harinder  and  Mausam},
  title     = {Bootstrapping for Numerical Open IE},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {317--323},
  abstract  = {We design and release BONIE, the first open numerical relation extractor, for
	extracting Open IE tuples where one of the arguments is a number or a
	quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency
	patterns that express numerical relations in a sentence. BONIE’s novelty lies
	in task-specific customizations, such as inferring implicit relations, which
	are clear due to context such as units (for e.g., ‘square kilometers’
	suggests
	area, even if the word ‘area’ is missing in the sentence). BONIE obtains
	1.5x yield and 15 point precision gain on numerical facts over a
	state-of-the-art Open IE system.},
  url       = {http://aclweb.org/anthology/P17-2050}
}