Understanding N-Gram in the context of NLP

2026-06-22 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, quick

Summary

The article explains N-gram models, a foundational Natural Language Processing (NLP) tool and a specialized Markov model. It describes how N-grams predict the next word in a sequence based on preceding words, similar to predictive text features on smartphones. The "n" in N-gram refers to the number of words grouped together in a "slice" of text. For instance, a 1-gram (unigram) considers words in isolation, like ["I"], ["love"], ["data"] from the sentence "I love data". A 2-gram (bigram) examines pairs of words, such as ["I love"] and ["love data"] from the same sentence. This concept is crucial for understanding basic language modeling in NLP.

Key takeaway

For NLP Engineers developing language models, understanding N-gram fundamentals is crucial for building basic predictive text features. You should recognize that N-grams, as specialized Markov models, group "n" words to predict the next in a sequence. This foundational knowledge helps you appreciate more complex models and troubleshoot issues related to contextual word prediction in your applications. Consider how varying "n" impacts model accuracy and computational cost.

Key insights

N-gram models predict the next word in a sequence by grouping "n" words, forming a foundational NLP tool.

Principles

• N-grams are specialized Markov models.
• "n" defines the word group size.
• Contextual words predict next word.

In practice

• Use unigrams for isolated word analysis.
• Use bigrams for word pair analysis.
• Apply for predictive text features.

Topics

• Natural Language Processing
• N-gram Models
• Markov Models
• Language Modeling
• Predictive Text
• Unigram
• Bigram

Best for: AI Student, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.