Zero Shot Entity Linking

Zero-shot entity linking (ZS-EL) tackles the challenge of connecting textual mentions to entities in a knowledge base without prior training on those specific entities. Current research focuses on improving both the candidate retrieval and ranking stages, employing techniques like read-and-select frameworks, coarse-to-fine lexicon-based retrieval, and graph-based methods to capture fine-grained information and handle the complexities of real-world, domain-specific knowledge bases. These advancements aim to address the limitations of existing models, particularly their struggles with unseen or low-frequency entities, and improve performance in conversational and biomedical contexts. The development of robust ZS-EL methods is crucial for various applications, including question answering, knowledge base population, and information extraction, particularly in scenarios with limited labeled data.