Corpus ID: 231855232

Bootstrapping Relation Extractors using Syntactic Search by Examples

  title={Bootstrapping Relation Extractors using Syntactic Search by Examples},
  author={Matan Eyal and Asaf Amrami and Hillel Taub-Tabib and Yoav Goldberg},
  • Matan Eyal, Asaf Amrami, +1 author Yoav Goldberg
  • Published 2021
  • Computer Science
  • ArXiv
  • The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs (Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We use these to obtain positive examples by… CONTINUE READING

    Figures and Tables from this paper


    Matching the Blanks: Distributional Similarity for Relation Learning
    • 138
    • Highly Influential
    • PDF
    Distant supervision for relation extraction without labeled data
    • 2,061
    • Highly Influential
    • PDF
    Bootstrapped Self Training for Knowledge Base Population
    • 29
    • Highly Influential
    • PDF
    Position-aware Attention and Supervised Data Improve Slot Filling
    • 221
    • Highly Influential
    • PDF
    Language Models are Unsupervised Multitask Learners
    • 2,471
    • Highly Influential
    • PDF
    DocRED: A Large-Scale Document-Level Relation Extraction Dataset
    • 55
    • Highly Influential
    • PDF
    Syntactic Search by Example
    • 4
    • PDF
    Snorkel: Rapid Training Data Creation with Weak Supervision
    • 320
    • PDF