Learning Causal Bayesian Networks from Text
This work aims to improve the automatic discovery of complex, conceptual-level causal relationships from text for AI systems, which is an incremental improvement over existing low-level extraction methods.
This paper addresses the challenge of automatically discovering causal relationships from text by proposing a method to infer conceptual-level causal relationships. It leverages concept hierarchies and linguistic variables to represent these relationships as a Causal Bayesian Network, demonstrating superior performance over existing approaches in inferring complex causal reasoning.
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.