N-gram model
A statistical shortcut: predict the next word based purely on the previous N-1 words, no deep learning required.
Think of it like
A fancy autocomplete that only remembers the last few words you typed: "The quick brown" -> probably "fox."
Example
Predict SMS text on a phone: if you type "see you," the next word is very likely "tomorrow" or "soon." A bigram model (N=2) learns these patterns from millions of texts.
How it actually works
An N-gram is a sequence of N words. A bigram model (N=2) says: given the word "the," what's the most likely next word? It's just counting — count how many times "the dog" appears, divide by total times "the" appears. Unigram, bigram, trigram, and higher-order n-grams capture increasingly distant context, but they hit a wall: they can't model long-range dependencies and they explode in memory (you need to count every possible sequence). Neural language models — especially Transformers — replaced n-grams because they learn from data more efficiently and handle long context.
For product teams
A clever statistical baseline that worked surprisingly well before deep learning; still fast for simple tasks.
For engineers
A Markov chain language model that estimates P(word_i | word_{i-N+1}...word_{i-1}) by counting occurrences in a corpus.
Related
- Language model — An ancestor of modern language models.
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