Listen: Named Entity Recognition
The NLP task of finding named things in text and classifying them as people, organisations, locations, dates, and products; the traditional pipeline sense of an entity.
Transcript
Named Entity Recognition, or NER, is how computers find and categorize named things in text. It takes unstructured writing and labels specific elements, like people, organizations, locations, and dates. For example, a system reading about a company founded by a specific person in a certain city will tag those individual pieces of information.
This process is a foundational step in turning ordinary prose into structured data. It feeds knowledge graphs and powers brand monitoring. But in the world of AI visibility, NER is even more critical. It is the mechanism that decides whether your brand is recognized as an entity at all.
When an AI system processes a page, a brand that is correctly recognized can be counted, linked, and associated with other concepts. If the system misses or mislabels your brand, it drops out of the knowledge graph entirely. This same extraction process is what allows us to map out a brand's association network and understand how AI models see your business.