@inproceedings{saha-etal-2017-bootstrapping,
    title = "Bootstrapping for Numerical Open {IE}",
    author = "Saha, Swarnadeep  and
      Pal, Harinder  and
      Mausam",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/P17-2050/",
    doi = "10.18653/v1/P17-2050",
    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."
}