What is Named Entity Recognition (NER)?
Named Entity Recognition (NER) is a natural language processing task that involves identifying and classifying named entities in text into predefined categories such as persons, organizations, locations, dates, monetary values, and other types. NER systems scan through text to find entity mentions and assign them to appropriate categories, providing structured information about who, what, where, and when from unstructured text.
Traditional NER systems used rule-based approaches or classical machine learning models trained on annotated datasets. Modern approaches predominantly use transformer-based models fine-tuned for entity recognition, or leverage large language models through prompting or few-shot learning. These systems can identify not just standard entity types but also domain-specific entities like gene names, chemical compounds, or product codes depending on their training or prompting.
NER is a foundational component in many AI agent systems, particularly those building knowledge graphs or entity-based memory. By identifying entities in conversations or documents, agents can track who and what is being discussed, build entity-centric knowledge representations, and enable entity-focused retrieval and reasoning. NER often serves as the first step in more complex information extraction pipelines that also identify relationships between entities and extract detailed attributes.