Natural language refers to the normal language 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 produce 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 at Epicor.
“AI also includes other areas in addition to NLP, such as computer vision, which deals with the analysis and generation of images, but advances in NLP in recent years have been at the heart of the current AI revolution,” says Stefan corpseauer, VP of Engineering, SandboxAQ.
He describes NLP as the analysis and generation of natural language using computers and says it is the use of Large Language Models (LLMs) and chatbots that are generating a lot of excitement around the topic.
NLP and LLMs
Volodymyr Kubytskyi, head of AI at MacPaw, delves even deeper, saying that popular LLMs like OpenAI's ChatGPT or Google's BERT are trained on massive amounts of text data, allowing them to use not just individual words, but also context to capture nuances and even creativity in language.
He argues that it is these LLMs that have taken NLP to new heights, enabling machines to generate coherent, human-like text, summarize long documents, translate between languages, and even engage in meaningful dialogue. By leveraging these models, NLP can now do things that seemed impossible just a few years ago, like writing essays or answering complex customer queries in a natural, fluid way.
“LLMs are the engine driving much of today’s progress in enabling machines to have human-like conversations,” says Kubytskyi. “This is AI meeting language at an incredibly sophisticated level.”
Why should companies care about NLP?
Corpse says that many of our business processes are encoded in natural language because natural language is the way we communicate with each other.
“Our reports and presentations, our internal memos and emails, and all of our customer communications are written in natural language,” says corpseauer. “NLP techniques can speed up and automate workflows that include all of these things.”
Building on this, Buzzalino explains that companies should care about NLP because it allows them to gain meaningful insights from unstructured text data like customer reviews, emails and social media posts.
He says NLP can help automate tasks such as customer support through chatbots, sentiment analysis for market research and efficient document processing, thereby improving efficiency and improving customer loyalty.
Sukh Sohal, Senior Consultant at Affinity Reply, agrees. He says NLP is having a real impact on businesses, changing the way they interact with customers, handle data and even communicate internally.
“Imagine an AI that can analyze thousands of customer messages in minutes and identify common issues, emotions or trends,” says Sohal. “For businesses, NLP can mean the difference between overwhelming customer service demands and efficient, responsive operations.”
He says NLP allows companies to automate repetitive tasks, improve customer experiences and dynamically respond to feedback, while freeing up human teams for tasks that require real insights.
Kubytskyi is passionate about the use of LLMs and how these NLP skills are improved. For example, he says customer service bots built on models like GPT can handle not only simple queries but also more nuanced, complex conversations. You can follow the flow of dialogue, understand the context, and respond in a way that feels more human than ever before.
“This level of understanding allows companies to offer personalized, responsive services without sacrificing efficiency,” says Kubytskyi.
NLP applications
NLP is 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 queries, machine translation services like Google Translate, sentiment analysis tools that measure public opinion on social media, and text analysis systems that extract important information from large amounts of documents as well as some real-world applications of NLP.
One real-world application of NLP that stands out to Corpsauer is as an intelligent assistant for writing code. This allows developers to work more efficiently and also enables low-code and no-code solutions that are more powerful than before.
How does NLP work?
Unlike traditional computing, which relies on simple commands, NLP is about teaching machines to understand the intricacies and idiosyncrasies of human language, including context, tone and meaning, says Sohal. In this way, AI is moving from strict rule-following to more intuitive understanding, opening up new opportunities for technology to interact with us in a more “human” way.
NLP is based on two key components. “There is Natural Language Understanding (NLU), which analyzes input to extract meaning and intent, and Natural Language Generation (NLG), which generates answers based on context and system logic,” says Dan Balaceanu, co-founder and chief product officer at DRUID AI.
For example, if a user requests “book a flight to London,” NLU identifies “book” as the action and “London” as the destination, while NLG generates a follow-up response such as “I found a flight to London.” £220. Would you like to book it?”
Technically, Sohal says, NLP works by breaking language down into patterns that computers can recognize. It starts with tokenization, which involves breaking sentences into words or smaller parts. Grammar and structure are then analyzed to understand the relationships between words.
Next comes semantics, where computers use massive amounts of data to capture meaning, even for slang or idioms. Finally, context and intent are added through machine learning, particularly deep learning. “Here, NLP models learn from large data sets to identify emotions, wishes or subtleties in language and thus make the answers more human,” says Sohal.
Balaceanu adds that this process standardizes vocabulary by reducing words to their root forms and filtering out common words that add little meaning. This helps identify the true intent of the prompt to respond to and how to respond.
He adds that to improve the accuracy of answers, NLP relies on machine learning techniques such as deep neural networks and models such as transformers like BERT.
“In order for NLP systems to respond accurately, they are trained on large data sets that include various language patterns, grammar rules and sentence structures, covering a range of possible queries and answers,” 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 different linguistic scenarios and allow it to recognize intent, context and nuance. Over time and through continuous learning from large, representative data sets, AI systems will become increasingly better at handling complex language tasks and providing relevant, human-like answers.