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

Manik Bhandari, Mark Feblowitz, Oktie Hassanzadeh, Kavitha Srinivas, Shirin Sohrabi

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.