@article{Bhandari_Feblowitz_Hassanzadeh_Srinivas_Sohrabi_2021, title={Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17876}, DOI={10.1609/aaai.v35i18.17876}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bhandari, Manik and Feblowitz, Mark and Hassanzadeh, Oktie and Srinivas, Kavitha and Sohrabi, Shirin}, year={2021}, month={May}, pages={15759-15760} }