Have you ever argued with your voice assistant? Maybe you asked Siri to set a timer for ten minutes and instead it called your ex. Or you typed a question into a chatbot and got an answer that sounded like it came from an entirely different conversation? Well, if you’ve done any of these things, you’ve interacted with a powerful and almost invisible form of artificial intelligence called Natural Language Processing. It works behind the scenes, acting as a translator, a summarizer, and even a creative partner.
And today, we’re pulling back the curtain on this technology. It might sound complicated, but the goal is simple: to teach computers our most human trait—no, not trying to scam each other out of money but rather, language.
I’m Chris Rouman, and I’m Nerd Adjacent.
What is NLP?
So, what exactly is Natural Language Processing–or NLP?
Let’s start with the name. “Natural Language” is just a technical way of saying human language—any language—with all its slang, its nuance, its typos, and its sarcasm. It’s how we actually talk and write. For me, mostly the sarcasm… and the typos. Lots and lots of typos.
The “Processing” part is what computers do. They process data. So, NLP is a field of computer science and artificial intelligence focused on giving computers the ability to read, understand, interpret, and even generate human language.
The key word here is understand. A computer can easily store a book, but NLP is what allows the computer to understand that the word “book” in the sentence “I need to book a flight” means something completely different than in “I’m reading a good book.” It’s the difference between just seeing words and understanding their meaning in context.
For a computer, our language is a foreign language. NLP is the massive, ongoing effort to make computers fluent in “Human.” It’s the bridge between our messy, creative, and often illogical way of communicating and the rigid, logical world of computer code.
You can think of NLP as a really patient translator who takes your messy, human language and turns it into something a computer can actually work with.
At its core, Natural Language Processing is a blend of linguistics (the structure and meaning of language) and machine learning (getting computers to learn patterns from data). To make machines understand us, developers feed models massive amounts of text — everything from books to websites to Wikipedia articles (for realz) — so they can start to recognize patterns in how we talk and write.
How NLP works
So, how do programmers even begin teaching a computer to understand language? Well, it happens in a few key steps.
First, the computer has to break our language down into pieces it can analyze. This is called tokenization. If you give it the sentence, “The quick brown fox jumps,” it chops each word into its own token. It’s like dicing vegetables before you start cooking—you need manageable pieces.
Second, the computer tries to understand the grammar and structure. It tags each token with its part of speech—is it a noun, a verb, an adjective? This helps it figure out that “fox” is the subject and “jumps” is the action. This is the basic grammar lesson. Kinda like in mad libs: for every noun, you use poop, and for every verb, you use farting.
But the real magic happens in the third step: understanding meaning. This is where modern AI models, especially something called Transformer models, come in. These are the engines behind tools like ChatGPT. These models are trained on gigantic amounts of text—we’re talking a huge chunk of the internet, millions of books, and articles like I mentioned earlier. By analyzing all this data, they learn relationships between words.
Two of the most important models behind the scenes are BERT and GPT. Don’t think of them as famous cohabitating puppets from your youth, but rather as different approaches to the same challenge: helping computers understand and use language the way we do.
GPT, which stands for Generative Pre-trained Transformer (woof)–developed by Open AI– is like your super-chatty–but totally not annoying at all–friend who can riff on anything. You give it a prompt — a question or a sentence – and it keeps going. It’s great at storytelling, writing emails, summarizing content, even writing code. It’s trained to predict the next word, so it’s all about flowing forward — word after word, after word, on and on… I mean, come on already.
Google’s BERT, on the other hand, is more like a careful reader. It takes a whole sentence — sometimes even an entire paragraph — and tries to understand the meaning of every word by looking at the full context. That makes BERT (aka Bidirectional Encoder Representations from Transformers–ugh) especially good at answering questions, figuring out the sentiment of a sentence, or helping search engines know what you’re really asking regardless of how embarrassing it might seem.
A fun way to think of this is to imagine language as a mystery novel. BERT reads the whole book to understand who the killer is — every sentence helps make sense of the story. GPT writes the next chapter — based on where the plot seems to be going. One model interprets, the other generates.
Together, models like BERT and GPT–beyond all their zany adventures–transformed natural language processing — which is just a fancy way of saying ‘getting computers to understand us.’ They made things like voice assistants, chatbots, smart replies, and even translation apps way more accurate, more natural, and just… more helpful.
So whether you’re asking your phone what time the store closes, or reading an AI-generated bedtime story (risky), chances are, BERT helped it understand the question — and GPT wrote the answer.
Why it matters
By now, you may already be thinking of all the places where this technology shows up in your daily life… because it does. NLP is everywhere including your pocket. Siri and Google Assistant use it to understand your commands. When you say, “Text Mom I’ll be 10 minutes late,” NLP figures out the intent (send a text), the recipient (Mom), and the content of the message.
Spam filters use NLP to analyze the content of an email and determine if it’s junk. When your email client suggests a reply like “Got it, thanks!”—that’s NLP in action.
It’s also breaking down global barriers and ushering in world peace! Well, maybe not that last part, but with the use of sophisticated NLP, tools like Google Translate allow two people who don’t speak the same language to have a conversation. Now you can confidently order a croissant and espresso in German from that French partisery located in the Italian part of Switzerland and not feel completely humiliated.
And it probably comes as no surprise that it’s being used in healthcare. Researchers are using NLP to scan millions of patient records or scientific papers to find patterns and accelerate medical discoveries.
Companies use it for something called sentiment analysis—scanning social media to understand public opinion about their products. Is the new thingamajiggy a hit or a miss? NLP can read thousands of reviews in seconds and give you the answer. Spoiler alert: it’s only so-so.
Essentially, natural language processing is taking the single largest source of human knowledge—our written and spoken language—and making it accessible, searchable, and useful in ways we’ve never seen before.
Conclusion
So, the next time you use a translation app, talk to your smart speaker, or let autocorrect ruin a perfectly good text message, you’re seeing NLP in action. It’s a field that’s not only changing the way we interact with machines—but also helping machines adapt to the incredibly rich, messy, and nuanced way we use language.
It’s by no means perfect. But it’s evolving quickly. And in the process, it’s quietly reshaping how we communicate, search, write, and even think about information.
Natural language processing doesn’t just help machines understand us. It also helps us better understand ourselves—what we say, how we say it, and what we really mean.
And while it’s not quite fluent in “human” just yet, it’s learning. Just like we are.
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