Event Structure

Event structure research focuses on understanding and representing the complex relationships between events within text and other data modalities, aiming to move beyond simple event identification to capture intricate nested, overlapping, and causally linked structures. Current research employs various approaches, including generative models, graph neural networks, and large language models adapted for tasks like event extraction and causal relation identification, often leveraging knowledge graphs and structured representations like Abstract Meaning Representation (AMR). This work is significant for advancing natural language processing, enabling improved information extraction, question answering, and narrative understanding, with applications ranging from biomedical text analysis to social bias detection in narratives.