Event Annotation

Event annotation, the process of tagging specific events and their attributes within data (e.g., text, audio), aims to facilitate automated understanding and analysis of complex information. Current research emphasizes developing large, diverse, and publicly available datasets for various event types, including those in under-resourced languages and domains like infant cries or Arabic narratives, often employing techniques like active learning to optimize annotation efficiency. These efforts leverage advanced models such as BERT and large language models (LLMs) for improved accuracy and scalability, impacting fields ranging from natural language processing and recommendation systems to social science research analyzing bias in narratives. The resulting annotated datasets and improved models are crucial for advancing research and building more robust applications across numerous domains.