Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)
Keywords:Knowledge Graph Construction, Applications, Information Extraction
AbstractIn 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.
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
AAAI Student Abstract and Poster Program