Named entity recognition (NER): What you need to know
As AI continues to develop and integrate into new industries, so comes with it a long list of terms and abbreviations that businesses and organizations will have to end up learning to keep up with the ever-changing technology.
Named Entity Recognition (NER) is one term that you might have seen used in discussions around AI, but what does it actually mean? Let's unpack what NER is, how it works, and why it matters more than ever in 2025.
What is named entity recognition (NER)?
Named Entity Recognition, or NER, is a subtask of information extraction. Its job is to scan unstructured text (emails, transcripts, news articles) and identify entities—specific pieces of information like names, dates, locations, organizations, and more.
Basically, it's how a machine starts to "understand" language the way humans do. We read a sentence like "Apple Inc. released the new iPhone on September 12 in Cupertino" and automatically know a company, a product, a date, and a location. NER teaches machines to do the same.
Common categories NER detects
Although exact categories can vary depending on the system, most NER models identify:
People (e.g., "Serena Williams")
Organizations (e.g., "UNICEF")
Locations (e.g., "New York City")
Dates and times (e.g., "July 4th, 2023")
Products (e.g., "iPhone 15")
Monetary values (e.g., "$3,000")
Some advanced systems go even deeper, tagging things like events, laws, medical terms, or even sensitive identifiers in legal or healthcare settings.
Remove sensitive details from videos in just a few clicks.
Why is NER important?
Information is messy, especially at scale. NER helps organizations extract structured meaning from massive volumes of text and audio data.
From automating compliance checks to powering redaction tools (we'll get into that), NER turns chaos into something manageable.
How named entity recognition works
At its core, NER involves:
Tokenization — Breaking the text into words or terms.
Part-of-speech tagging — Labeling words as nouns, verbs, etc.
Entity classification — Using machine learning models (or rule-based systems) to tag tokens as entities.
Modern NER systems often use deep learning techniques, including transformers like BERT, which allow them to detect entities even in ambiguous or informal contexts.
For example: "I met Jeff in DC last Thursday" would yield "Jeff" as a person, "DC" as a location, and "last Thursday" as a date—even though none of that is spelled out clearly.
Real-world applications of NER
Where is NER actually making a difference?
In business and finance
NER helps financial institutions process vast amounts of documents—think annual reports, contracts, market news—to extract relevant information quickly. Traders and analysts might use it to track mentions of companies or events in real time.
In healthcare
Medical records are packed with patient names, medications, conditions, and timelines. NER is vital for safeguarding privacy, especially when paired with redaction tools.
In legal and compliance
Law firms and regulators use NER to scan legal documents for entities like case numbers, client names, and jurisdictional information. It's also essential in privacy compliance.
In customer support and chatbots
Ever asked a chatbot about a refund and had it understand your situation instantly? NER is often behind that. It allows virtual agents to pick out who you are, what you're asking, and any relevant dates or numbers.
Benefits of using NER
Efficiency: Automates what used to take hours (or days)
Accuracy: Reduces human error, especially when paired with AI oversight
Scalability: Works just as well on one email as on 10,000 reports
Compliance: Especially important when paired with AI in video redaction software
Privacy Protection: Automatically flags and removes sensitive data
Challenges and limitations
No system is perfect, and NER struggles when:
Entities have ambiguous or context-dependent names ("Jordan" can be a person or a country)
There are multiple overlapping entities
The text is noisy (think bad audio recordings or typo-ridden transcripts)
That said, AI models are getting better all the time—especially when built on large, diverse datasets.
Final thoughts
Ultimately, NER is one of the most foundational aspects of AI; it quietly powers so many of the tools we now rely on daily—from chatbots to compliance software.
And as we continue to deal with more data than ever, tools like Pimloc's Secure Redact are leading the way in making that data safer, smarter, and more manageable. Whether you’re redacting footage, transcribing emergency calls, or analyzing legal transcripts, NER redaction helps you quickly redact specific words or phrases in audio and transcripts (and more securely).
Want to explore how NER powers cutting-edge redaction solutions? Learn more about our redaction software designed for policing and compliance by reaching out to one of our team members today.