Named Entity Recognition NLP

Named Entity Recognition (NER) is an NLP task that identifies and classifies named entities in text. Entity types vary by application but commonly include: Person, Organization, Location, Date, Money, Facility. Input: "Elon Musk leads Tesla in Austin." Output: Elon Musk (PERSON), Tesla (ORG), Austin (LOC). NER is built using sequence labeling models (BiLSTM-CRF, Transformers) that tag each word as entity type or non-entity. Pre-trained models (spaCy, Hugging Face) work well on generic text; fine-tuning improves domain-specific accuracy.