Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)

Authors

  • Manik Bhandari Carnegie Mellon University
  • Mark Feblowitz IBM Research
  • Oktie Hassanzadeh IBM Research
  • Kavitha Srinivas IBM Research
  • Shirin Sohrabi IBM Research

Keywords:

Knowledge Graph Construction, Applications, Information Extraction

Abstract

In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKNowLI which only takes as input the text corpus and extracts a high-quality collection of cause-effect pairs in an automated way. We approach this problem using state-of-the-art natural language understanding techniques based on pre-trained neural models for Natural Language Inference (NLI). Finally, we evaluate the proposed method on existing and new benchmark data sets.

Downloads

Published

2021-05-18

How to Cite

Bhandari, M., Feblowitz, M., Hassanzadeh, O., Srinivas, K., & Sohrabi, S. (2021). Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15759-15760. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17876

Issue

Section

AAAI Student Abstract and Poster Program