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Paste text and click Generate// extract unigram, bigram & trigram lists from text
Generate unigram, bigram, or trigram frequency lists from any text. Free browser-based N-gram analyzer with sortable results and CSV export.
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Paste text and click GenerateDrop any text into the input area — articles, emails, books, code comments, or any corpus you want to analyze.
Select unigrams (single words), bigrams (word pairs), trigrams (3-word phrases), or 4/5-grams for longer sequences.
Click Generate, sort by frequency or alphabet, then copy as CSV or download the full results file.
An N-gram is a contiguous sequence of N words from a body of text. Unigrams are single words, bigrams are two-word pairs, and trigrams are three-word sequences. Analyzing N-grams helps reveal recurring phrases, writing patterns, and keyword density in any text corpus.
An N-gram is a contiguous sequence of N items (usually words) extracted from a text. "N" is a variable — unigrams are N=1, bigrams are N=2, trigrams are N=3, and so on. N-grams are foundational to natural language processing, search engines, and predictive text systems.
A unigram is a single word ("hello"). A bigram is a two-word sequence ("hello world"). A trigram is three consecutive words ("hello world today"). Higher N values capture longer phrases, giving more context but appearing less frequently in typical text.
Stopwords are common English words like "the", "a", "is", "in", "and" that carry little semantic meaning. Enabling the stopword filter removes these tokens before generating N-grams, helping surface more meaningful phrases rather than filler words.
Yes — the tool handles any Unicode text including Vietnamese, French, German, Spanish, and more. However, the built-in stopword filter is English-only. For other languages, leave stopword filtering disabled and manually review common filler words in your results.
The minimum frequency filter hides N-grams that appear fewer than N times in your text. Set it to 2 to show only repeated phrases, or higher to focus on the most prevalent patterns. This is useful for large documents where rare one-off phrases clutter your results.
Your text is processed server-side via a simple PHP script with no logging or storage. Nothing is saved after the response is returned. For maximum privacy on sensitive documents, consider using a local text analysis tool instead.
An N-gram generator is a text analysis tool that extracts all contiguous word sequences of a given length from a body of text, then counts how often each sequence appears. The result is a ranked frequency list that reveals which words and phrases dominate your content — a powerful lens for SEO research, NLP preprocessing, content auditing, and linguistic analysis.
Whether you are a digital marketer analyzing competitor content, a developer preprocessing training data for a language model, or a researcher studying literary style, N-gram frequency analysis gives you an objective, quantitative view of language patterns that manual reading simply cannot match.
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The three most commonly used N-gram sizes each serve distinct analytical purposes:
The process behind N-gram generation is straightforward but powerful. Given an input text, the tool first tokenizes it — splitting the text into individual word tokens by splitting on whitespace and removing punctuation. Optionally, tokens matching a common stopword list are removed. Then, a sliding window of width N moves across the token list one step at a time, yielding every consecutive N-word sequence. Finally, identical sequences are grouped and counted, and the resulting frequency map is sorted from most to least common.
For example, given the sentence "the quick brown fox jumps over the lazy dog", the bigrams extracted are: "the quick", "quick brown", "brown fox", "fox jumps", "jumps over", "over the", "the lazy", "lazy dog". With stopword filtering enabled, "the quick", "over the", and "the lazy" would be removed, leaving more semantically meaningful pairs like "quick brown" and "brown fox".
Search engine optimization has evolved far beyond single keyword stuffing. Modern search algorithms analyze phrase-level patterns — exactly what N-gram analysis surfaces. By running your top-performing articles through a bigram or trigram generator, you can identify the natural phrases your audience uses, find gaps between your content and competitor content, and optimize for long-tail search queries that are specific enough to convert.
Content strategists use bigram analysis to audit their writing for repetitive phrasing, weak vocabulary diversity, or over-reliance on filler constructions. Trigram analysis helps identify branded phrases, recurring calls-to-action, and structural patterns in high-performing content that can be replicated across new pieces.
In machine learning and NLP, N-grams are a foundational feature extraction technique. Before the transformer era dominated NLP, N-gram language models were the state of the art for tasks like speech recognition, machine translation, and spelling correction. Even today, N-gram features are used in:
When reading your N-gram frequency table, a few patterns are worth watching for. In a well-written, varied text, you expect a steep frequency curve — a few very common phrases, then a long tail of phrases appearing only once or twice. If your top bigrams all appear hundreds of times, your text may be highly repetitive or template-driven. If almost every N-gram appears exactly once, your text may lack cohesive themes or recurring concepts.
The percentage column shows each N-gram's share of the total N-gram count. A bigram with a 5% frequency in a 1,000-word article is occurring far more than chance would predict — it is almost certainly a deliberate or structural pattern worth investigating. Use the minimum frequency filter to cut noise and focus on N-grams that appear at least 2–5 times, depending on your text length.
This tool provides CSV export so you can take your N-gram data into spreadsheet software, Python, R, or any other analysis environment. The exported CSV includes the N-gram text, raw count, and percentage frequency — ready for pivot tables, visualizations, or further processing with pandas, Excel, or Google Sheets. Click "Copy CSV" to copy the data to your clipboard, or "Download" to save a .csv file directly.