Causal Knowledge Extraction through Large-Scale Text Mining


  • Oktie Hassanzadeh IBM Research
  • Debarun Bhattacharjya IBM Research
  • Mark Feblowitz IBM Research
  • Kavitha Srinivas IBM Research
  • Michael Perrone IBM Research
  • Shirin Sohrabi IBM Research
  • Michael Katz IBM Research



In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.




How to Cite

Hassanzadeh, O., Bhattacharjya, D., Feblowitz, M., Srinivas, K., Perrone, M., Sohrabi, S., & Katz, M. (2020). Causal Knowledge Extraction through Large-Scale Text Mining. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13610-13611.