Causality Extraction

Causality extraction focuses on automatically identifying and classifying cause-and-effect relationships within text, aiming to build structured representations of causal knowledge. Current research emphasizes improving accuracy and efficiency in extracting these relationships, particularly addressing challenges like handling complex sentences, long-range dependencies, and ambiguous phrasing, often leveraging large language models (LLMs) and graph-based methods. This field is significant for advancing knowledge representation and reasoning across diverse domains, including medicine, finance, and climate science, enabling more sophisticated analysis and prediction capabilities.