Cross Document

Cross-document coreference resolution focuses on identifying and linking mentions of the same entities or events across multiple documents, a crucial task for building knowledge graphs and improving information retrieval. Current research emphasizes developing efficient algorithms, often leveraging large language models and contrastive learning techniques, to handle the computational challenges posed by large document collections and complex relationships. This work is driven by the need for improved accuracy and scalability in applications ranging from historical text analysis to scientific literature understanding and question answering systems that require multi-document reasoning. The resulting advancements promise to significantly enhance knowledge extraction and reasoning capabilities across diverse domains.