Jsut a slaml twon gril lvii’n in a lnoley wlord She took the mgnidhit tiarn gniog aynhwree

Jsut a ctiy boy bron and reiasd in Stuoh Dteiort He took the mhidgint tiran gonig awyenhre

Even before you realized these are the opening lines to Journey’s “Don’t Stop Believin’,” you could probably read it with no issue. That’s because our brains are capable of processing language in some cool, interesting ways! Imagine if computers could do the same? Spoiler alert: they can.

Enter natural language processing (NLP) — the technology behind tools like OpenAI’s ChatGPT and Google’s Gemini. In a process very similar to the way we process language, NLP empowers computers to understand and generate human language, making interactions with AI feel like you’re talking to a real person. Exciting, right?

So what exactly is natural language processing and how does it work?

Table of Content

• What is Natural Language Processing?
• How Does Natural Language Processing Work?
• Tokens: The Building Blocks of NLP
• Natural Language Processing Tasks
• Natural Language Processing Use Cases (+ Examples!)
• Getting Started with NLP

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What is Natural Language Processing?

Natural language processing, or NLP, is a dynamic branch of artificial intelligence (AI) that empowers computers to understand, interpret, and generate human language in an intentional way. You’ve probably already experienced chatting with a virtual assistant or chatbot that feels almost human and responds as if it really understands you — that’s the magic of NLP at work!

At its core, NLP combines linguistics and machine learning to sift through massive amounts of text and speech data. This data enables machines to tackle a variety of tasks, such as ‘understanding’ context, performing sentiment analysis, translating languages, and summarizing information. Natural language processing is crucial for making sense of unstructured data, (essentially any data that isn’t neatly organized) and making it easier to parse. Consider all those unstructured data sources out there — like emails, social media posts, or even voice recordings. NLP is the key to helping machines try and make sense of this chaotic data.

Better yet, NLP isn’t just limited to commonly spoken global languages like English or Spanish. While it hasn’t quite expanded to fictional languages like High Valyrian or Dothraki from Game of Thrones (yet!), it can process programming languages like JavaScript and Python. This opens up a whole new world for developers, where they can leverage NLP to generate code using tools like Codex and GitHub Copilot, making programming more accessible and efficient.

One of the most groundbreaking developments in NLP has been the rise of transformer models. Transformers are a specific type of generative AI model used in large language models (LLMs) that excel at processing sequential data — like the order of words in a sentence — allowing AI to understand and generate text almost instantly.

NLP is a vital — and I mean extremely vital — part of generative AI, which relies on NLP to understand input data. Without NLP, machines wouldn’t be able to process human language or provide meaningful answers. So whether you’re interested in building chatbots, creating smarter AI applications, or developing tools that bridge the gap between humans and machines, mastering NLP is more than a good idea — it’s essential.

How Does Natural Language Processing Work?

From the top-level, NLP is all about teaching computers to be better at ‘understanding’ human language. Let’s say you walk into a party fashionably late and your friends are already deep in conversation. To successfully join in, you can try to pick up on the context of the conversation, understand everyone’s different tones, and then respond appropriately. That’s kind of what NLP does — just with algorithms and data instead of snacks and music!

Let’s break down NLP and how it works in a few steps.

• Step 1: Gathering Data

The first step in NLP involves collecting large amounts of text and speech data. This data can come from a variety of sources, like social media posts, websites, or transcripts of conversations. Think of this stage as gathering ingredients for a recipe. The quality and type of ingredients (data) you choose will impact the quality of the final dish (output data).

• Step 2: Tokenization

Once you have your data, the next step is breaking it down into smaller pieces, or “tokens.” (More on tokens later!) This is like chopping vegetables for a salad to make it easier to eat. By separating each word or phrase from its larger context, you make it easier for the machine to grasp the individual components of the language.

• Step 3: Understanding Context

The next step in NLP is understanding context, and this is where it can get a little tricky (for humans, too!). NLP uses different techniques to uncover the meaning behind tokens. Take the acronym “BTS,” for example. In one context, it could mean “behind the scenes,” while in another, it might refer to the global music group. Advanced language models use techniques like word embeddings — think of these as maps of word relationships — to try and determine the appropriate meaning based on the surrounding words.

• Step 4: Learning Patterns

To interpret and generate language, NLP relies on machine learning models. These models learn from the data by identifying common patterns. For example, a model might learn that the phrase “I love” is often followed by positive words while “I hate” usually leads to negative ones. Modern NLP uses deep learning techniques, particularly neural networks, to enhance understanding and creating data. As you might imagine, this isn’t a perfect system yet!

• Step 5: Finally, once the model ‘understands’ the input, it can generate responses — much like writing a reply in a conversation. The model combines its learned knowledge and context to produce a response that makes sense and feels (hopefully) natural.

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Tokens: The Building Blocks of NLP

To really understand how NLP works, you have to understand how tokens work. It’s a lot more fascinating than you probably think, we promise. Remember, in NLP, text is broken down into tokens (words, phrases, or symbols) that let the computer analyze and understand the language. Each token has meaning and context, making it easier for algorithms to identify patterns and relationships, which is critical for some of the NLP tasks and use cases that we’ll get into later. Without tokens, NLP wouldn’t be able to understand the nuances of human language.

For a large language model to process any text input, it has to be taught how to process natural language (aka how to understand what you mean when you write, in English, “What’s the weather today?”). And in order to make sense of that string of 24 characters and question mark, the AI model has to first break the data down into the tokens.

Tokens can represent one character, a few characters, or an entire word depending on the word. Let’s keep using English for the example for now; we have our own building blocks for processing language — letters of course, but also words and phrases that we can process quickly and build into sentences and paragraphs.

A lot of our words and phrases contain common letter patterns. Take “ing” for example: thing, swing, going, bingo, tinge, etc. We’re able to recognize those letter patterns because we were taught how these common patterns behave in language, so they’ve become common and familiar, allowing those of us who can read to read more quickly.

In addition, our brains have other ways of recognizing words and understanding meaning that are also all about pattern recognition. Take, as an example, this paragraph of text:

I cnduo’t bvleiee taht I culod aulaclty uesdtannrd waht I was rdnaieg. Unisg the icndeblire pweor of the hmuan mnid, aocdcrnig to rseecrah at Cmabrigde Uinervtisy, it dseno’t mttaer in waht oderr the lterets in a wrod are, the olny irpoamtnt tihng is taht the frsit and lsat ltteer be in the rhgit pclae. The rset can be a taotl mses and you can sitll raed it whoutit a pboerlm. Tihs is bucseae the huamn mnid deos not raed ervey ltteer by istlef, but the wrod as a wlohe. Aaznmig, huh? Yaeh and I awlyas tghhuot slelinpg was ipmorantt! See if yuor fdreins can raed tihs too.

(Editor’s note: we are assuming a lot of neuronormative standards in this blog but we know not everyone works this way!) Computers process data much differently than human brains do, and because of this, models have their own way of interpreting our human language — you guessed it, tokens! Tokens bridge the gap between what we understand and input as natural language and a format that the model can understand. By breaking words into tokens, the learning model is able to start recognizing patterns not only in the letters and words themselves, but also in their context and relationships to one another.

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Natural Language Processing Tasks

Using tokens is just scratching the surface. NLPs use a variety of tasks and techniques that allow computers to decode and interact with human language, whether it’s through text or speech. Here are some key tasks that play a role in the process:

Sentiment Analysis

Sentiment analysis is all about identifying the emotional tone behind text. It helps determine whether a statement is positive, negative, or neutral. For instance, when I check Yelp reviews before visiting a restaurant, I’m probably looking for restaurants that have glowing reviews like “The food was amazing!” over the ones that state, “I didn’t enjoy the service.” Businesses use sentiment analysis to gauge customer opinions and spot trends on social media, giving them valuable insights into public opinion.

Named-Entity Recognition (NER)

Think of named-entity recognition as a personal assistant that highlights important information in a document. NER identifies and categorizes key elements, such as people, organizations, and locations. For example, in the sentence “John van Hengel founded Feeding America in Phoenix, Arizona,” NER would recognize “John van Hengel” as a person, “Feeding America” as an organization, and “Phoenix, Arizona” as a location.

Part-of-Speech Tagging

Part-of-speech tagging assigns grammatical labels to each word in a sentence — nouns, verbs, adjectives, etc. You can think of it as understanding the roles of players on a team where each word has a specific function that contributes to the overall meaning of the sentence.

Word Sense Disambiguation

Word sense disambiguation tackles the tricky job of figuring out which meaning of a word is intended in a given context. For example, in the sentence “The CIA found a mole in their organization,” the AI needs to understand that “mole” refers to a spy, not the furry creature. As you might imagine, making the correct interpretation is fairly mandatory for true natural language processing.

Speech Recognition

Speech recognition converts spoken language into text. It’s what powers virtual assistants like Siri and Alexa. By understanding spoken commands, NLP makes technology more accessible and user-friendly.

By mastering these NLP tasks, you’ll be well on your way to developing apps that can understand and engage with people.

Natural Language Processing Use Cases (+ Examples!)

If you’re thinking about diving into the world of AI development, getting to know the different use cases of NLP can set you up for success. Understanding how giants like Google and Amazon use NLP in the real world can give you a creative boost and help you build a strong foundation for your future AI projects.

Text generation: NLP is the driving force behind generative AI, enabling computers to produce human-like text based on your input. Imagine having a tool that can write stories, generate articles, or even draft your emails. Well, the future is now. Models like OpenAI’s GPT-3 can create everything from poems to code and even TV scripts — all from a simple prompt.

Translation: Ever used a translation app while traveling? If you have, you’ve taken advantage of one of the many powers of NLP. Tools like Google Translate can instantly convert text and speech across an impressive 243 languages. It’s not perfect yet, ask any translator — translating isn’t just about swapping words one-for-one. It’s about capturing the essence and tone of the original message, and accounting for cultural and linguistic nuances. A classic example demonstrates this perfectly: when translating the Bible passage “The spirit is willing, but the flesh is weak,” from English to Russian and back, it turned into “The vodka is good, but the meat is rotten.” It makes sense culturally — spirit (read: vodka) and flesh could be meat — but it definitely doesn’t have the same meaning. Luckily, with today’s advances in NLP, Google Translate can return the original phrase accurately — trust me, I checked!

Spam detection: NLP is essential for filtering out unwanted spam messages. Gmail is getting pretty good at this, for example. It uses machine learning algorithms to analyze patterns and keywords in emails, automatically classifying them as spam. This keeps your inbox neat and tidy, protecting you from phishing attempts that aim to steal your personal information.

Customer service: Chatbots have become a staple in customer service, thanks to NLP. Companies like Amazon use NLP to power virtual assistants like Alexa and customer service chatbots. These systems can understand customer queries and provide relevant answers, significantly cutting down on wait times and ensuring simple questions get answered quickly and correctly so more complex issues can be escalated more smoothly to a human agent.

Text prediction and autocorrect: You’ve almost certainly noticed suggestions popping up while typing on your phone or computer (it’s happening right now as I write this blog), you’re still experiencing NLP firsthand. Tools like SwiftKey, Grammarly, and the autocorrect feature in Google Docs analyze your typing patterns and context to suggest words or fix mistakes. So, when I type my name — Jouviane Alexandre — on Google Docs, it no longer appears with a squiggly line indicating it’s misspelled, and you don’t have to manually add it to your personal dictionary any more!

Information retrieval: Search engines like Google rely heavily on NLP to improve user queries and deliver relevant results. When you type a question, NLP helps the search engine understand your intent, ensuring you find exactly what you’re looking for — even if your phrasing isn’t spot on.

Getting Started with NLP

It’s clear that NLP is already a huge part of both our everyday lives and the business world. From chatbots that offer instant customer support to advanced translation tools that break down language barriers, NLP is transforming how we interact with tech and each other. If you’re feeling inspired to dive into this exciting field, now is the perfect time to start!

As a developer, it’s never too late to explore the world of generative AI and leverage its powerful tools. Start by jumping into our Break Into Tech program to learn some of the most in-demand skills for AI careers. This course will give you the knowledge and tools you need to harness the power of NLP in your projects and open doors to new, innovative ideas that can change the way we work and live. So, why wait?