Temporal Relation

Temporal relation extraction focuses on identifying and modeling the temporal relationships between events, crucial for natural language understanding and various applications. Current research emphasizes improving accuracy and robustness, particularly in handling ambiguity and limited data, through techniques like multi-label classification, retrieval-augmented generation with large language models (LLMs), and graph neural networks for representing temporal dependencies. These advancements are significant for improving machine comprehension of narratives, facilitating tasks such as event forecasting, knowledge graph completion, and cross-user activity recognition in diverse fields like healthcare and urban planning.