Text Graph

Text graphs integrate textual information with graph structures to represent data and relationships, aiming to leverage both semantic and structural insights for improved learning. Current research focuses on developing efficient transformer-based models and self-supervised learning frameworks for text graph representation, often incorporating graph neural networks (GNNs) and techniques like curriculum learning and knowledge distillation to enhance performance, particularly in low-data scenarios. These advancements are improving performance in various applications, including link prediction, attribute inference, and relation extraction, demonstrating the value of combining textual and structural information for complex data analysis. The resulting improvements in model efficiency and generalization capabilities are significant contributions to the fields of natural language processing and machine learning.