Relation Extraction

Relation extraction, a core natural language processing task, aims to identify and classify relationships between entities within text. Current research heavily focuses on improving the robustness and efficiency of relation extraction models, particularly using transformer-based architectures like BERT and LLMs, often incorporating techniques like attention mechanisms, graph convolutional networks, and retrieval-augmented generation to handle complex scenarios such as long sentences, ambiguous relations, and low-resource settings. These advancements are crucial for building knowledge graphs, powering information retrieval systems, and enabling more sophisticated applications in diverse fields like biomedical research and document understanding.