How does NLP work?
The process of teaching computers how to understand and respond to our language is a complicated process involving a few key steps:
Breaking down language: The first step is tokenization, where a computer learns to break sentences down into words and phrases, making it easier to understand each individual component.
Learning structure: Then, it analyzes these smaller pieces and figures out how grammar connects them to one another and gives meaning to the whole.
Interpreting meaning: Next, the computer learns how to understand the meaning behind the words. It begins to incorporate context (what certain words mean in certain situations) and determine intent (what the author of the text or voice command is trying to convey).
Responding to prompts: Once it has an understanding of how language works, the model gains the ability to respond appropriately to the input it receives.
Learning over time: NLP incorporates machine learning, meaning the more examples of human language it encounters and analyzes, the better it gets at producing accurate responses. Over time, the computer essentially learns how to read, listen and talk and becomes capable of interacting with us in increasingly natural ways.
The progress from NLP to LLMs
The evolution from traditional NLP to large language models (LLMs) is like tracing a family tree. Early NLP systems were the ancestors, built with rules and simple statistical models to analyze text in chunks like words or phrases. They laid the foundation but lacked the “genes” to understand context or complexity, limiting their ability to handle real-world language.
LLMs are descendants that have inherited the progress of their predecessors and evolved into something far more powerful. With transformers as their “genetic breakthrough” in 2017, LLMs gain the ability to process language holistically, understanding context and meaning in ways that earlier generations could not. Trained on vast datasets, these models excel at everything from storytelling to nuanced conversations, marking a leap in the lineage of language technology.
How NLP impacts our daily lives
Most of us interact with NLP every day, often without even realizing it. The technology is the powerhouse behind voice assistants, allowing them to understand and act on voice commands. Every time you ask your device to play your favourite song, it's NLP that makes it happen. The technology also enables chatbots to intuitively engage with customers and helps search engines return results that are relevant to the queries they receive.
When you’re travelling abroad and need an app on your phone to make a quick translation, NLP enables the process. It also handles predictive text, helping you send messages to friends and family more quickly than you otherwise might. NLP, at its heart, simplifies and streamlines our everyday lives.
Real-life success stories
The business world, not surprisingly, has been quick to realize the time and money that can be saved by using NLP to automate a range of previously time-consuming tasks. Examples include:
Simplifying Legal Work: Evisort is one of a host of companies making life easier on legal teams by automating the review of contracts and other documents. The company’s tools quickly pinpoint important clauses, flag potential risks and even suggest changes, saving hours of manual effort.
Improving Customer Support: Tars and other startups are using smart chatbots to handle everyday customer inquiries. These bots can respond to questions, solve common problems and provide helpful information. This frees up support teams to focus on more complicated tasks.
Streamlining Hiring: With platforms such as Pymetrics, recruiters can match job candidates to roles based on skills, experience and cultural fit. This helps companies spend less time sifting through resumes and more time talking to qualified candidates.
Common misconceptions about NLP
As with any new technology or concept, there are a few misunderstandings about NLP.
It understands language the way we do: NLP teaches computers how to interpret meaning from voice and text but it does this differently than our brains do. Computers lack inherent knowledge of meaning and context and, instead, use statistical patterns to infer the meaning behind words.
It is unbiased: NLP models can learn and reflect any biases that are contained in the data used to train it.
Larger models are better: Size is just one of the factors that underpin an NLP’s ability to accurately communicate. Bigger is not necessarily better.
NLP models are not effective: When used properly, NLP can be a very useful tool for personal and professional development.
How you can use NLP
If you’re interested in learning more about NLP, there are a few good ways to get started.
Online NLP tools
Google offers a free platform called Colab that allows users to write and execute Python code in their browser. Python is often used for NLP projects because it is relatively simple to use and provides access to extensive libraries. Kaggle is another free platform that lets the online community get hands on experience with NLP fundamentals. It provides a beginner-friendly notebook that helps newcomers understand how to use Python to perform simple tasks.
Pre-built applications
Using ChatGPT or another, similar chatbot is a great way to get hands-on experience with NLP. Try using one of these platforms to summarize text (or websites) and generate your own creative content.
Online courses
Coursera and DataCamp offer guided instruction to help novices learn the basics of NLP through coding exercises and other practical projects.
The Evolving World of NLP
NLP is likely to change the way we work, learn and communicate with technology. It will make a wide range of tasks, such as reviewing contracts, diagnosing medical issues or creating personalized learning tools, faster and easier and it might help break down language barriers and improve accessibility for people with disabilities. These advancements come with challenges, however, including concerns over privacy, biases in AI systems and the need for thoughtful oversight to avoid misuse. While NLP tools will likely make many tasks more efficient, they also raise questions about their impact on jobs, creativity, and decision-making in society.