N-Grams and Markov Assumptions: The First Predictive Models of Language

2026-04-10 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

N-grams represent one of the earliest predictive frameworks in Natural Language Processing (NLP), emerging around March 15, 2026, to address the problem of predicting the next word in a sequence. This approach treats language as a dynamic sequence rather than a static object, moving beyond simple text representation to statistical prediction. An n-gram is a sequence of "n" consecutive words (e.g., unigram for one word, bigram for two). N-gram models estimate the probability of a word appearing next by counting its occurrences after a specific preceding sequence in a large text corpus. This method relies on the Markov assumption, which posits that the next word depends only on a limited recent window of "n-1" words, simplifying computational complexity. While effective for short-range patterns, n-grams face significant limitations, including the sparsity problem due to the exponential growth of possible word sequences, and an inability to generalize beyond exact observed patterns or capture long-range dependencies and semantic relationships.

Key takeaway

For an AI Scientist or NLP Engineer studying foundational language models, understanding n-grams is crucial for grasping the evolution of predictive text. You should recognize how their statistical, count-based approach and the Markov assumption laid the groundwork for modern models while simultaneously exposing fundamental challenges like sparsity and the need for long-range context. This knowledge informs why subsequent models, like neural networks, were necessary to overcome these inherent limitations.

Key insights

N-grams were the first predictive language models, using statistical counts over short word sequences to anticipate the next word.

Principles

• Language modeling can be framed as next-word prediction.
• Probability can be estimated from observed textual experience.
• Local context is useful but insufficient for full language understanding.

Method

N-gram models estimate conditional probabilities of next words by counting exact word sequences in a corpus, applying the Markov assumption to limit context to the preceding n-1 words.

In practice

• Use n-grams for tasks requiring short-range pattern recognition.
• Recognize n-gram limitations for long-range dependencies.
• Understand sparsity challenges in large vocabularies.

Topics

• N-grams
• Markov Assumption
• Next-Word Prediction
• Language Modeling
• Sparsity Problem

Best for: AI Scientist, NLP Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.