What is natural language processing (NLP)?

In the early days of AI, natural language processing was hit or miss. Frustrated employees could be heard shouting at their screens when the system misunderstood their prompts. But today, NLP has come of age. With the rapid advancement of AI and machine learning technologies, you can now ask your system a plainly worded question and get back a useful, structured response every time. And when NLP is integrated into your systems, it can access data and information across the business, giving users results that are not only accurate but robust and actionable.

NLP meaning and definition 

Natural language processing (NLP) is a type of artificial intelligence that allows computers to understand, interpret, and generate human language. It draws on computational linguistics, machine learning, and statistical models to make language usable by machines, not just in terms of words but also meaning, intent, and tone. NLP allows people to interact with systems through natural speech or text, instead of relying on technical commands or fixed inputs.

How does NLP work?

Natural language processing starts by breaking language into pieces the computer can process – such as words, parts of speech, or even syllables. It then assigns meaning to those pieces based upon what it’s learnt. Data is the food that helps all AI and machine learning systems grow because just like humans, the more examples a system encounters, the better it gets at spotting how language typically behaves.

Most NLP uses a type of AI called deep learning. These kinds of systems rely on more than a set of fixed rules – they learn instead from vast amounts of real-world language scenarios. As they evolve, they begin to capture not just syntax and grammar, but tone, nuance, and context. Deep learning models do not treat each word in isolation. They look at how words relate to one another across an entire sentence or paragraph which helps them to understand intent and deeper meaning.

Older NLP systems had to establish context and relationships on a word-by-word basis. Results were slow and unsophisticated. Today, language processing technologies use transformer-based models. These models read whole chunks of text at once, rather than one piece at a time. This allows them to pick up on subtle cues such as sarcasm or confusion, and to extrapolate meaning from only partial technical instructions.

What are the core capabilities of NLP?

Natural language processing brings together a variety of techniques to help machines interpret and generate human language. Deep learning is driving many recent NLP breakthroughs. But the more traditional rule-based and statistical approaches still play a major role in many enterprise use cases.

NLP vs. NLU vs. NLG: What’s the difference? 

All the techniques listed above can be applied to a variety of different models and use cases. But fundamentally, NLP is a bit of a two-sided coin. To function optimally, it must be able to make sense of human language inputs and be equally capable of generating outputs that those same humans can easily understand. NLU (natural language understanding) and NLG (natural language generation) represent the two sides of this linguistic coin.

• Natural language understanding (NLU) describes sentiment analysis, intent detection, and speech recognition. It focuses on interpretation, even when language is vague or ambiguous. It makes it possible for systems to detect mood, recognise entities, extract meaning, and know when two phrases mean the same thing.
• Natural language generation (NLG), on the other hand, handles the generation of natural language to explain and summarise complex ideas. It can amalgamate disparate data formats – compiling dry, linear reports or charts with open-ended text. NLG creates fluent, logical, and user-friendly wording from almost anything.

NLP vs. LLM: How do they relate? 

NLP is the broader field. It includes any method for helping machines understand, interpret, or generate human language whether spoken or written. Large language models (LLMs) like ChatGPT are one way to do that. They use deep learning to handle complex NLP tasks like answering questions, generating content, or interpreting inconsistent input. But they don’t replace all of NLP. Tools like speech recognition, tokenization, or grammar parsing still play key roles alongside LLMs. These days LLMs pretty much dominate the conversation, but in practise, they’re actually part of a larger toolkit.