Event Relation Extraction

Event relation extraction (ERE) focuses on identifying and classifying the relationships between events within text, aiming to understand the complex narratives and causal structures inherent in natural language. Current research emphasizes improving the accuracy and efficiency of ERE across diverse relation types (e.g., temporal, causal, coreference, subevent) using various approaches, including graph neural networks, large language models (LLMs), and encoder-decoder architectures enhanced with techniques like prototype matching and cluster-aware compression. Advances in ERE are crucial for numerous applications, including improved information retrieval, question answering systems, and the automated construction of knowledge graphs representing real-world events.