Hi mom and dad,
It’s me again. I think you understood my last blog explaining neural networks, so I thought I’d keep going and talk about something called natural language processing—NLP for short.
I realize it’s probably not something you’ve heard much about, but it’s a very cool concept, so I thought I’d break it down for you in a way that’s easy to understand.
What is natural language processing?
At its core, NLP is a type of technology that helps computers understand human language. You know how when you talk to your phone using Siri or Google Assistant, they seem to “get” what you’re saying? That’s NLP! It’s the bridge between human language and computer language.
Why is it important?
People speak in a very complex way—we use slang, varying cadences, and different tones. Computers, on the other hand, rely on strict instructions. NLP allows us to talk to computers naturally, without needing to use complicated codes or commands.
Imagine if instead of programming a computer to answer a question with, “if x happens, then do y,” you could just ask it: “What’s the weather like today?” And it would understand what you mean and respond appropriately. This is what NLP makes possible.
How does it work?
NLP involves several steps to help computers understand us:
- Breaking down sentences: When we speak or write, the computer first breaks down our words into smaller parts, i.e. individual words or phrases.
- Understanding the meaning: Then it tries to understand the meaning behind those words. It looks at context. For example, if you say, “I’m feeling blue,” it has to figure out if you’re talking about a color or an emotion.
- Responding back: Once it understands, it can respond in a way that makes sense—either with an action (like playing a song) or by giving you an answer.
Everyday uses of NLP
You might not realize it, but NLP is everywhere. Some examples you probably use daily include:
- Smart assistants like Siri, Alexa, etc. that understand voice commands.
- Chatbots that help answer questions on websites.
- Email filters that move junk mail to the spam folder based on the content.
- Language translation tools like Google Translate, which can convert your text into another language.
How does IDVerse use NLP
At IDVerse, we use NLP in a number of ways:
- Document data extraction: When users take a photo of their identity document, NLP helps analyze the text within these documents. It accurately reads the data—such as names, addresses, and dates—by converting captured images or text fields into readable digital information.
- “Smart masking” of data: This technique is designed to protect personal or confidential information (like social security numbers) by showing only a portion of the data or replacing sensitive parts with characters like asterisks (e.g. displaying a social security number as “***-**-1234”)
- Fraud detection: NLP quickly and accurately extracts machine-readable zone (MRZ) data from ID documents. This allows us to cross-check the extracted information with that provided on the document.
- Language ability and transliteration: NLP tools can translate the text automatically into a familiar language, allowing IDVerse to verify documents in any language efficiently.
- Named entity recognition (NER): NER is a key task in NLP and a powerful tool that helps us by identifying and classifying document types, layout and structure.
So the next time you ask Alexa to play your favorite song or get Siri to remind you of something, you’ll know there’s a lot of smart technology at work behind the scenes, thanks to NLP.
Love,
Emily
About the post:
Images and videos are generative AI-created. Prompt: A smiling 65 year old black woman sitting on a wooden bench in Central Park, New York holding a small cute anthropomorphic chatbot in her hand. The woman and the chatbot both wave at the camera. Lush green trees and park scenery in background. Photorealistic style, soft natural lighting, 4K detail, depth of field. Tools: Midjourney, Luma.
About the author:
Emily Hendley is Senior Vice President of Enterprise Product Management for IDVerse. With over a decade and a half of experience in business analysis and product management, she has an extensive track record of delivering enterprise solutions that drive growth and enhance user experience, particularly in the onboarding space. Emily joined IDVerse in 2021.
