Entity Disambiguation Benchmark

Entity disambiguation benchmarks evaluate the accuracy of systems that link mentions of entities in text (e.g., names, places) to their corresponding entries in a knowledge base. Current research focuses on improving accuracy, particularly for challenging scenarios like historical texts and low-resource languages, using diverse approaches including contrastive learning, encoder-decoder models that leverage entity descriptions, and methods that incorporate structural information from knowledge graphs. These benchmarks are crucial for advancing entity linking, a fundamental task with broad applications in information retrieval, knowledge graph construction, and natural language processing more generally.