Event Argument

Event argument extraction focuses on identifying and classifying the participants (arguments) and their roles within events described in text or other modalities like images. Current research emphasizes improving accuracy and efficiency through various approaches, including graph neural networks that model event-argument interactions and their contextual relationships, question-answering systems that leverage large language models, and generative models that perform zero-shot or few-shot learning. These advancements are crucial for numerous applications, such as knowledge base population, question answering, and opinion mining, by enabling more accurate and comprehensive understanding of events and their contextual nuances.