Entity Normalization

Entity normalization, a crucial task in natural language processing, aims to map unstructured mentions of entities (e.g., diseases, genes, institutions) in text to standardized identifiers within a knowledge base. Current research emphasizes improving accuracy and efficiency using various approaches, including large language models (LLMs) augmented with retrievers, transformer-based models, and bi-encoder architectures for ranking candidate entities. This work is vital for advancing biomedical research, enabling better data integration and analysis across diverse datasets, and facilitating the development of more robust and reliable healthcare applications.