N-gram Language Model
Also known as: n-gram model, statistical language model, N-gram Dil Modeli
An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.
Key highlights
- Simple, fast, and interpretable — probabilities come directly from observed counts.
- A strong, transparent baseline for text generation, spelling correction, and speech recognition.
- Low computational cost compared with neural language models, with no specialised hardware required.
Intuition
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How it works
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When to use it
Use an n-gram model when you have a reasonably large text corpus (the source suggests at least about 100 documents) and a task framed as predicting or explaining word sequences — autocompletion, spelling correction, or as a baseline language model. It needs enough training text to estimate reliable counts, and smoothing must be applied to handle the sparsity of unseen sequences.
Strengths & limitations
- Simple, fast, and interpretable — probabilities come directly from observed counts.
- A strong, transparent baseline for text generation, spelling correction, and speech recognition.
- Low computational cost compared with neural language models, with no specialised hardware required.
- Captures only short-range context (the previous n−1 words) and cannot model long-distance dependencies.
- Suffers from data sparsity: most word sequences are unseen, so smoothing is mandatory.
- Model size and memory grow quickly as n increases, while higher-order n-grams become increasingly sparse.
Common pitfalls
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Applications
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Frequently asked
What does the 'n' in n-gram mean?
It is the number of consecutive words considered together. A bigram (n=2) conditions the next word on the single previous word, a trigram (n=3) on the previous two. Larger n captures more context but needs far more training data and becomes sparse.
Why is smoothing necessary?
A finite corpus can never contain every plausible word sequence, so many valid n-grams have a count of zero and would receive zero probability. Smoothing methods such as Laplace add-one or Kneser-Ney reassign a small probability to unseen sequences so the model does not break on new text.
How much training data do I need?
The source suggests at least about 100 documents as a minimum. More text gives more reliable counts; higher-order n-grams in particular need substantially more data to avoid sparsity.
How does an n-gram model differ from a neural language model?
An n-gram model estimates next-word probabilities directly from counts over a fixed window of previous words, making it simple, fast, and interpretable. Neural models learn distributed representations and capture longer context, at higher computational cost. N-grams remain a useful transparent baseline.
Sources
- 1.Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.
- 2.Chen, S.F. & Goodman, J. (1999). An Empirical Study of Smoothing Techniques for Language Modeling. Computer Speech & Language, 13(4), 359-394.
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Cite this page
ScholarGate. (2026, June 1). N-gram Language Model. ScholarGate. https://scholargate.app/text-mining/ngram-language-model