Document Graph

Document graphs represent textual data as interconnected nodes (e.g., words, sentences, entities) and edges reflecting relationships, enabling richer semantic understanding than traditional sequential models. Current research focuses on developing effective graph construction methods, integrating graph neural networks (GNNs) with pre-trained language models, and applying these techniques to tasks like document classification, information extraction, and relation extraction. This approach offers improved performance in handling long documents, complex layouts, and out-of-distribution data, with significant implications for various natural language processing applications.