💬 What Is NLP?
Natural Language Processing (NLP) is the branch of AI that gives computers the ability to work with human language — reading it, understanding its meaning, and even producing it. Sounds simple? Language is actually one of the hardest problems in AI.
Language is full of sarcasm, idioms, pronouns, spelling errors, cultural references, and ever-changing slang. Every sentence is a puzzle. NLP is the set of tools — from simple word counts to massive neural networks — that lets machines start solving that puzzle.
✂️ Tokenization: Chopping Text Into Pieces
Before a machine can "read" text, it needs the text as a list of standardised pieces it can process. We call each piece a token. Tokenization is the act of splitting text into tokens.
un + believ + able. This helps handle rare words — even words the model has never seen can be built from familiar parts.
After tokenization, each token gets a unique integer ID. The sentence "The cat sat." might become [482, 1751, 992, 13]. The model works with those numbers, not letters.
Example: tokenizing the sentence below (simple whitespace + punctuation split)
Hover a chip to see a sample token ID (illustrative).
🔢 Turning Words Into Numbers: Embeddings
Neural networks only understand numbers. So we need a way to turn every word into a number — or better, a vector (a list of numbers). The simplest approach, Bag of Words, gives each word a slot in a giant array: 1 if the word appears in a document, 0 if not. It works, but it loses order and meaning.
A much richer idea: Word Embeddings. Train the model so that each word maps to a compact vector of, say, 300 numbers, learned such that similar words end up near each other in that vector space.
king − man + woman ≈ queen
Notice: king/queen have similar first three values (royalty dimension). dog/cat are similar in the fourth (animal dimension). Embeddings capture these patterns automatically from data.
🛠️ What Can NLP Do?
NLP powers a huge range of applications you use every day:
Type a sentence or short review below. The detector uses a built-in lexicon of positive and negative words, handles simple negation ("not good"), and shows which words influenced the score.
This is a bigram Markov model — the simplest possible language model. It learned, from a short built-in corpus, which words tend to follow each other. Pick a starting word, choose a length, and watch it generate text by repeatedly sampling the most likely next word. This is the same core idea behind GPT — just at a microscopic scale.
How it works: The model scanned every pair of consecutive words in the corpus. When generating, it looks up the current word, picks a random successor weighted by how often each one appeared, then repeats. Bigger models do the same thing — just with much more data and context.