Event Relation

Event relation extraction (ERE) focuses on identifying and classifying the relationships between events within text or multimedia data, encompassing temporal, causal, coreference, and subevent connections. Current research emphasizes improving the accuracy and efficiency of ERE using large language models (LLMs), often incorporating graph neural networks or prototype-based methods to capture complex inter-event dependencies and semantic nuances. Challenges remain in handling long-distance relations, mitigating knowledge conflicts and biases within LLMs, and scaling to large, diverse datasets like MAVEN-ERE. Advances in ERE are crucial for numerous applications, including natural language understanding, knowledge base construction, and event prediction across various modalities.