What is an N-gram?
At its core, an n-gram is simply a sequence of ‘n’ items from a given sample of text or speech. The ‘items’ could be characters, syllables, or most commonly, words. So, if you take the phrase “SEO is essential,” here’s how it breaks down:
- Unigram (n=1): “SEO”, “is”, “essential”
- Bigram (n=2): “SEO is”, “is essential”
- Trigram (n=3): “SEO is essential”
This concept isn’t new. In fact, it dates back to the early 20th century, with roots in the work of Andrey Markov and later, Claude Shannon, who both explored how sequences can predict what comes next in a string of text. In the digital age, n-grams have become a foundational tool in natural language processing (NLP) and search technology.
Why Are N-grams Important?
Here’s the thing, language is messy. People don’t always use the same words or phrases, and context is everything. N-grams help computers “chunk” language into manageable pieces, making it easier to spot patterns, predict what comes next, and understand meaning at a basic level.
Think of n-grams as the “training wheels” for machines learning language. They allow algorithms to:
- Predict the next word in a sentence (think autocomplete on your phone)
- Spot common phrases and collocations (useful for keyword research and content optimisation)
- Detect and correct spelling mistakes
- Power basic sentiment analysis and document classification
What makes n-grams especially valuable is their simplicity and speed. While deep learning models now dominate headlines, n-grams still play a crucial role behind the scenes-especially when you need fast, interpretable results or when working with smaller datasets.
Where Are N-grams Used?
You might be surprised at just how many places n-grams pop up:
And that’s just the tip of the iceberg. N-grams underpin everything from chatbots to automatic translation, making them a staple in the toolkit of anyone working with language data.
Can AI Help with N-grams? Absolutely.
Now, here’s where it gets interesting. While n-grams themselves are a statistical tool, AI has supercharged what we can do with them.
And let’s not forget, many of the breakthroughs in AI language models (think GPT, BERT) were built on insights first discovered using n-grams. Even today, research shows that mixing n-gram features with neural architectures can improve performance in certain tasks so they’re not going anywhere soon.
How N-grams Can Power Up Your SEO, Paid Marketing, and Socials
Let’s get practical. Here’s how n-grams those simple word patterns actually help your marketing across the board:
N-grams for SEO: Topical Content, Better Rankings
N-grams for Paid Marketing: Higher ROI, Lower Wasted Spend
N-grams for Socials: Content That Resonates
Bottom line
N-grams aren’t just a technical curiosity, they're a practical tool for making your marketing more targeted, efficient, and effective. If you’re not using n-grams, you’re missing out on the patterns that drive real business results.
Implementing N-gram Models: Quick Tips
If you’re looking to get hands on, here’s what you need to know:
- Tokenisation
Start by breaking your text into words or characters. - Choose Your ‘n’
Unigrams for basic analysis, bigrams/trigrams for more context. - Handle Sparsity
Use smoothing techniques (like Laplace smoothing) to deal with rare or unseen n-grams. - Leverage Tools
Python libraries like NLTK, spaCy, or even scikit-learn make building n-gram models straightforward.
Remember, the best approach is often to experiment and try different n-gram sizes, mix them with other features, and see what works for your data.
The Future: N-grams and the AI Revolution
So, are n-grams yesterday’s news? Not at all. While AI has brought us powerful new tools, n-grams remain a practical, proven solution. Especially for SMEs and anyone looking for speed, simplicity, and transparency. They’re the unsung heroes that help bridge the gap between raw text and machine intelligence.
If you’re serious about making sense of language data whether for SEO, content marketing, or the next big AI project, don’t overlook n-grams. They may not be flashy, but they get the job done.
Key takeaway: In a world obsessed with the latest AI breakthroughs, sometimes the smartest move is to master the basics. N-grams are one of those basics: reliable, effective, and still at the heart of how machines understand language.
Want to go deeper? Check out resources from Stanford NLP, Google AI, and this hybrid NLP research for more on how n-grams and AI work hand-in-hand.
