Temporal Question Answering

Information continuously evolves over time. Time becomes a fundamental dimension that shapes how we extract, retrieve, interpret, and reason about knowledge. As information systems are constantly updated, models must determine not only what is relevant, but also when that information is valid. This lecture provides a structured overview of Temporal Question Answering (TQA) task in which answers to user questions require careful consideration of the temporal aspects of requested information. We examine the core principles underlying the identification and normalization of time expressions, time-aware document ranking, and temporal reasoning in retrieval-augmented generation (RAG). By connecting classical extraction and IR foundations with modern LLM-based reasoning, this lecture presents an up-to-date perspective on temporal information systems.