Event Extraction

Event extraction, a core natural language processing task, aims to automatically identify events and their constituent elements (arguments) within text. Current research emphasizes handling complex argument structures, including implicit and scattered information, often employing large language models (LLMs) and question-answering approaches, sometimes enhanced by reinforcement learning or graph-based methods. This field is crucial for various applications, including knowledge graph construction, information retrieval, and real-time event monitoring (e.g., epidemic surveillance), driving advancements in both theoretical understanding and practical deployment of NLP technologies. Ongoing efforts focus on improving model robustness, addressing evaluation inconsistencies, and expanding to multilingual and multimodal contexts.