Semantic Association

Semantic association research focuses on understanding and modeling how concepts relate to each other, aiming to improve the performance and interpretability of AI systems. Current research emphasizes developing methods to capture these associations within various model architectures, including graph-based models, language models enhanced with hierarchical semantic information, and those leveraging dynamic activation to improve efficiency. This work is crucial for advancing AI capabilities in tasks such as question answering, text-to-image generation, and cross-document relation extraction, ultimately leading to more robust and explainable AI systems.