Named Entity Recognition

Named entity recognition (NER) extracts structured mentions — people, organizations, locations, dates — from unstructured text, making it foundational to knowledge graphs, financial compliance, and clinical NLP. CoNLL-2003 English F1 scores have been above 93% since BERT, and current leaders like UniNER and GLiNER push past 95%, but these numbers mask the real difficulty: nested entities, emerging entity types, and cross-lingual transfer where performance drops 10-20 points. The shift from sequence labeling to generative NER (framing extraction as text generation) has opened the door for LLMs to compete, though latency-sensitive production systems still rely on encoder models like DeBERTa-v3 and SpanBERT.