Have you ever started typing a sentence on your phone and seen it suggest the next word before you even type it? Or wondered how search engines seem to know what you're about to say? Behind these “smart guesses” lies a powerful idea from natural language processing (NLP) called the n-gram.
In this blog post, we'll break down what n-grams are, how they help computers model language, and why they matter in applications like autocomplete, machine translation, and text prediction.
1. What Is an N-Gram?
An n-gram is simply a sequence of “n” words.
Let’s see a few examples:
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Unigram (n=1): single words
“The”, “cat”, “sat”
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Bigram (n=2): two-word sequences
“The cat”, “cat sat”
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Trigram (n=3): three-word sequences
“The cat sat”, “cat sat on”
So if you have a sentence like:
“The cat sat on the mat”
The bigrams are:
[“The cat”, “cat sat”, “sat on”, “on the”, “the mat”]
And the trigrams are:
[“The cat sat”, “cat sat on”, “sat on the”, “on the mat”]
Each n-gram captures a small slice of how words follow one another in real language.
2. Why Use N-Grams?
N-grams form the basis of one of the simplest yet most powerful tools in NLP: the n-gram language model.
A language model is a system that assigns a probability to a sequence of words. In plain terms, it tries to predict what comes next in a sentence.
Let’s try it out:
“I am feeling very ___”
What would you expect next?
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“happy”
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“tired”
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“cold”
You probably wouldn’t guess “refrigerator” or “banana” — they’re syntactically valid, but unlikely in context. That’s the core idea of a language model: some word sequences are more likely than others, and we can learn those patterns from data.
An n-gram model does this by assuming the next word depends only on the last few words, not the entire sentence. This is known as the Markov assumption.
3. A Real-World Analogy
Think about how we learn language as humans. If someone says:
“I want to eat…”
Your brain instantly starts filtering probable options: “pizza”, “rice”, “something”, etc.
That’s an instinctive language model at work! An n-gram model tries to mimic this by looking at lots of real sentences, counting how often words follow each other, and using that to guess what’s likely.
For instance, let’s say we analyzed a collection of restaurant reviews and found:
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“I want to eat pizza” — 120 times
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“I want to eat sushi” — 80 times
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“I want to eat salad” — 30 times
So if our model sees:
“I want to eat ___”
It will predict “pizza” as the most likely word, based on the data.
4. The Power and Limits of Simplicity
N-gram models are:
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Simple: easy to build and understand
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Fast: require just counting and storing word sequences
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Effective: work well for many applications like autocomplete or spell-check
But they also have limitations:
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They can’t look far back in the sentence (usually only 1–3 words)
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They struggle with rare or unseen phrases
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They don’t understand meaning — just statistics
For example, a bigram model wouldn’t know that “eat rock” is weird, even if it appeared once in a dataset.
Later models (like BERT or GPT) overcome this by using deep learning, but n-grams are still a foundational idea in NLP.
5. Where Are N-Grams Used Today?
Even in the age of neural networks, n-grams are used:
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In search engines to autocomplete or correct queries
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In speech recognition to disambiguate similar sounds
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In assistive typing and augmentative communication devices
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In spam filtering, where certain sequences of words can be tell-tale signs
They also play a key role in educational tools, corpus linguistics, and even poetry analysis!
Conclusion
N-gram models are the ABCs of computational language understanding. They help machines guess what's likely to come next — just like humans do — by learning from real-word usage patterns.
Understanding n-grams not only gives you insight into the past of NLP but also a stepping stone into more advanced topics like neural language models, transformers, and beyond.
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