Causal Relation Extraction

Causal relation extraction (CRE) focuses on automatically identifying cause-and-effect relationships within text, a crucial task for understanding complex narratives and improving the explainability of AI systems. Current research emphasizes developing robust models, often leveraging pre-trained language models like BERT and incorporating graph attention networks or hybrid approaches combining deep learning with knowledge-based methods, to accurately extract causal information from diverse sources, including biomedical literature, social media, and even autonomous vehicle reports. The ability to effectively extract causal relationships has significant implications for various fields, enabling improved knowledge discovery, enhanced AI interpretability, and more informed decision-making in safety-critical applications.