Joint Extraction

Joint extraction in natural language processing aims to simultaneously identify entities and their relationships within text, improving efficiency and accuracy compared to sequential approaches. Current research focuses on developing sophisticated neural models, often employing transformer architectures and techniques like multi-task learning, to better handle noisy data and complex interactions between entities and relations, including addressing challenges posed by overlapping entities and long-range dependencies in document-level extraction. These advancements are significant for knowledge graph construction, information extraction, and various downstream applications requiring structured information from unstructured text, such as question answering and medical knowledge extraction.