Natural language refers to the regular speech and text that we use to communicate with each other. Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language.
NLP bridges the gap between human communication and computer understanding by combining computational linguistics with machine learning, explains Arturo Buzzalino, Chief Innovation Officer, Epicor.
“AI includes other domains besides NLP, such as computer vision which deals with analysis and generation of images, but advances in NLP in the last few years have been at the heart of the current AI revolution,” says Stefan Leichenauer, VP of Engineering, SandboxAQ.
Describing NLP as the analysis and generation of natural language with computers, he says, it is the use of Large Language Models (LLMs) and chatbots that are driving a lot of the excitement around the subject.
NLP and LLMs
Drilling down further, Volodymyr Kubytskyi, Head of AI in MacPaw, says popular LLMs like OpenAI’s ChatGPT or Google’s BERT, are trained on massive amounts of text data, allowing them to grasp not just individual words but context, nuance, and even creativity in language.
He argues that it is these LLMs that have pushed NLP to new heights, enabling machines to generate coherent, human-like text, summarise long documents, translate between languages, and even engage in meaningful dialogue. By leveraging these models, NLP can now do things that seemed impossible a few years ago, like writing essays or answering complex customer inquiries in a natural, flowing manner.
“LLMs are the engine that’s driving much of today’s progress in making machines capable of human-like conversations,” says Kubytskyi. “This is AI meeting language at an incredibly sophisticated level.”
Why should businesses care about NLP?
Leichenauer says because natural language is the way we communicate with each other, a lot of our business operations are encoded in natural language.
“Our reports and presentations, our internal memos and emails, and all of our customer communications are written in natural language,” says Leichenauer. “NLP techniques can accelerate and automate workflows involving all of these things.”
Building on this Buzzalino explains businesses should care about NLP because it allows them to extract meaningful insights from unstructured text data like customer reviews, emails, and social media posts.
NLP, he says, can help automate tasks such as customer support through chatbots, sentiment analysis for market research, and efficient document processing, thereby improving efficiency and enhancing customer engagement.
Sukh Sohal, Senior Consultant at Affinity Reply agrees. He says NLP brings real impact to businesses by transforming how they engage with customers, handle data, and even communicate internally.
“Imagine an AI that can analyze thousands of customer messages in minutes, picking up on common issues, emotions, or trends,” says Sohal. “For companies, NLP can be the difference between overwhelming customer service demands and an efficient, responsive operation.”
He says NLP lets businesses automate repetitive tasks, improve customer experience, and respond dynamically to feedback while freeing up human teams for tasks that require real insight.
Kubytskyi is excited about the use of LLMs and how it’s elevating these NLP capabilities. For instance, he says, customer service bots powered by models like GPT can handle not just basic queries, but more nuanced, complex conversations. They can follow the flow of dialogue, understand context, and respond in a way that feels more human than ever before.
“This level of understanding allows businesses to offer personalized, responsive services without sacrificing efficiency,” says Kubytskyi.
NLP applications
NLP has become so integrated into our lives that we often overlook it.
Buzzalino points to virtual assistants like Siri and Alexa that understand voice commands, customer service chatbots that handle inquiries, machine translation services like Google Translate, sentiment analysis tools that gauge public opinion on social media, and text analytics systems that extract key information from large volumes of documents, as some real-world applications of NLP.
One real-world application of NLP that strikes Leichenauer is as a smart assistant for writing code. This enables developers to operate more efficiently and also allows for low-code and no-code solutions that are more powerful than before.
How does NLP work?
Unlike traditional computing, which relies on straightforward commands, NLP involves teaching machines to grasp the subtleties and quirks of human language, including context, tone, and meaning, says Sohal. It’s how AI moves from rigid rule-following to more intuitive understanding, opening up new ways for tech to interact with us in a more “human” way.
NLP is built on two key components. There’s Natural Language Understanding (NLU), which analyses input to extract meaning and intent, and Natural Language Generation (NLG), which produces responses based on context and system logic, says Dan Balaceanu, Co-Founder & Chief Product Officer at DRUID AI.
For example, when a user requests to “book a flight to London,” NLU identifies “book” as the action and “London” as the destination, while NLG generates a follow-up response, like “I found a flight to London for £220. Would you like to book it?”
Technically speaking, Sohal says, NLP works by breaking language down into patterns computers can recognize. It starts with tokenization, where sentences are split into words or smaller chunks. Then, grammar and structure are analyzed to understand the relationships between words.
Semantics come next, where computers use massive data to grasp meanings, even for slang or idioms. Finally, context and intent are added through machine learning, especially deep learning. “Here, NLP models learn from large datasets to identify emotions, requests, or subtleties in language, making responses more human-like,” says Sohal.
Balaceanu adds this process standardizes vocabulary by reducing words to their root forms and filtering out common words that add little meaning, which helps to identify the real intent of the prompt that it should respond to, and how it should answer.
He adds that to improve the accuracy of the responses, NLP leans on machine learning techniques, such as deep neural networks, and models like transformers such as BERT.
“For NLP systems to respond accurately, they are trained on vast datasets that include diverse language patterns, grammar rules, and sentence structures, covering a range of possible queries and responses,” adds Arunkumar Thirunagalingam, Manager, of Enterprise Data Management at Santander Consumer USA.
He says this training involves machine learning models and deep learning techniques that expose the AI to various linguistic scenarios, enabling it to recognize intent, context, and nuances. Over time, and with continuous learning from large, representative datasets, AI systems become more adept at handling complex language tasks and providing relevant, human-like responses.