Numerical Relation Extraction with Minimal Supervision

Authors

  • Aman Madaan Visa Inc.
  • Ashish Mittal IBM Research
  • . Mausam Indian Institute of Technology Delhi
  • Ganesh Ramakrishnan Indian Institute of Technology Bombay
  • Sunita Sarawagi Indian Institute of Technology Bombay

DOI:

https://doi.org/10.1609/aaai.v30i1.10361

Abstract

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity ( e.g., atomic_number(Aluminium, 13), inflation_rate(India, 10.9%)). This task presents peculiar challenges not found in standard IE, such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.

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Published

2016-03-05

How to Cite

Madaan, A., Mittal, A., Mausam, ., Ramakrishnan, G., & Sarawagi, S. (2016). Numerical Relation Extraction with Minimal Supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10361

Issue

Section

Technical Papers: NLP and Machine Learning