NLP

Named Entity Recognition (NER)

Teaching an AI to find and label important things in text, names, companies, dates, locations, dollar amounts.

Definition

An NLP task that finds and classifies named entities in text into predefined categories such as people, organizations, locations, dates, and monetary values. A NER system both locates the span of text and assigns it a label, so a sentence like 'Sophizo signed a deal in March' yields an organization and a date. Modern NER uses transformer models that judge each word from its surrounding context.

Why it matters

NER is the foundation of information extraction. It auto-populates CRM records from emails, pulls key terms and dates out of contracts, flags entities in news for monitoring, and structures messy text so downstream systems can act on it without manual data entry.

Related terms in NLP

BERT

Google's breakthrough AI model that reads sentences in both directions at once to understand context better.

Chain of Thought (CoT)

Asking an AI to "show its work" and think step-by-step, which makes it much better at solving math and logic problems.

Context Window

The maximum amount of text an AI can read and consider at one time, like how many pages of notes it can hold in its head.

Conversational AI

AI that can have natural back-and-forth conversations with humans, chatbots, voice assistants, and customer service bots.

Back to the full glossary

From vocabulary to outcomes

Ready to put Named Entity Recognition (NER) to work?

Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.

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