What is Natural Language Processing?
15 min read
Imagine talking to someone who speaks every language but understands none of them. That's Natural Language Processing. This technology bridges human communication and machine understanding, turning our messy, context-rich language into something machines can process.
How does AI understand human language?
Every time you type a prompt or ask AI a question, you participate in an ambitious project. We're teaching machines to engage with human language. This doesn't happen through rigid commands or programming code. It happens through natural, fluid conversation.
Natural Language Processing (NLP) bridges human communication and machine understanding. AI systems like ChatGPT or Claude transform our messy, context-rich language into something machines can process. They then convert mathematical calculations back into human-readable responses. This happens in three steps.
Step 1: Tokenization. The AI breaks language into analysable pieces called tokens. Think of this like chopping a sentence into smaller, bite-sized pieces. Words, parts of words, or punctuation. The AI examines each piece individually.
Step 2: Pattern Recognition. The AI examines how these pieces fit together. It spots patterns and understands which words relate to each other. Like solving a puzzle.
Step 3: Meaning Extraction. The AI extracts meaning from these relationships. It doesn't understand like humans do. But it learns what certain patterns usually mean. For example, it knows "I'm hungry" often leads to talking about food.
What makes this different from human understanding?
When you say "I'm hungry" you understand hunger through personal experience. AI understands it as a pattern of words associated with certain responses and contexts.
Consider these two sentences:
"The restaurant's atmosphere was warm and inviting."
"The restaurant's oven was warm and working."
To a human, the word "warm" means something completely different in each sentence. One is metaphorical, suggesting comfort and pleasantness. The other is literal, referring to temperature.
NLP systems must learn to navigate these nuances. Not through genuine understanding. Through analysing patterns in how humans use these words in different contexts.
This brings us to one of the most remarkable aspects of modern NLP. Its ability to appear so human-like in responses that we often forget we're talking to a machine.
How does AI handle uncertainty?
We naturally understand and work with uncertainty. Concepts like "pretty soon" or "almost ready" make perfect sense to us.
Traditional computer logic only works in absolutes. Yes or no. 1 or 0. True or false.
This created a fundamental challenge. How could machines work with human language when so much of what we say isn't absolute?
This is where fuzzy logic comes in. It's a way for computers to work with uncertainty. To understand that something can be partially true or belong to multiple categories at once.
Think of temperature. Humans think "It's pretty warm today." Traditional computer logic can only understand "Temperature > 25°C = HOT, Temperature < 25°C = NOT HOT." Fuzzy logic can understand "23°C is somewhat warm, 27°C is quite warm, 32°C is very warm."
This ability to work with degrees of truth rather than absolute values is crucial for understanding human language.
How does AI understand emotions and sentiment?
Consider the following:
"I'm happy"
"I'm delighted"
"I'm not unhappy"
"I couldn't be better"
"This is fine"
Each conveys a different shade of positive sentiment. Some, like "this is fine," might even convey the opposite depending on context and tone.
Sentiment analysis is how NLP systems attempt to understand these emotional nuances in language.
This isn't just about identifying positive or negative emotions. Modern NLP systems analyse multiple dimensions. Emotional tone. Intensity of feeling. Underlying intentions. Cultural context. Potential irony or sarcasm.
The technology does this not by truly understanding emotions. By analysing vast amounts of human communication to identify patterns.
When someone says "Oh yeah, this is fine" in a clearly negative situation, the system recognises this pattern as potential sarcasm. It bases this on how humans typically use this phrase in similar circumstances.
Where do you encounter NLP in daily life?
NLP is already woven into the fabric of our daily digital interactions. Often in ways we don't notice.
Digital Assistants. When you ask Siri about the weather, it uses NLP to understand your question. It figures out which location you're talking about. It responds in a way that makes sense to you.
Email and Text. Smart compose features suggest how to complete your sentences. Spam filters understand the content and intent of messages. Automatic email categorization sorts messages into primary, social, or promotional content.
Search Engines. Search engines understand what you're looking for even when your query isn't perfectly formed. They recognise the context of your search terms. They provide relevant results based on the intent behind your words.
Content Analysis. Automatic summarisation of long documents. Key point extraction from text. Translation services that maintain context and meaning.
The true power of NLP lies not just in these individual applications. It's in how they work together to create a more intuitive way of interacting with technology.
What should you remember about NLP?
As we move forward, the line between human and machine communication will continue to blur. This makes it increasingly important to understand both the capabilities and limitations of these systems.
The most important thing to understand about NLP is this. When you're talking to an AI, it's like talking to someone who has read every conversation ever written down but has never actually experienced the world.
AI can recognise patterns in how humans use words and respond accordingly. But it doesn't truly understand concepts the way we do through lived experience.
It's more like having an incredibly well-read conversation partner. One who can only draw from what others have written or said before.
This understanding becomes crucial as we explore Large Language Models.