N-gram Extractor
Quick Tips
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Extract word sequences (bigrams, trigrams, etc.) from text.
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Examples
The cat sat on the mat. The cat was happy.
Bigrams: the cat: 2 cat sat: 1 sat on: 1 on the: 1 the mat: 1 cat was: 1 was happy: 1
I think therefore I am
Bigrams: i think: 1 think therefore: 1 therefore i: 1 i am: 1 Trigrams: i think therefore: 1 think therefore i: 1 therefore i am: 1
Why Use This Tool?
What problems does this solve?
N-grams (word sequences) reveal common phrases and patterns in text. Extracting them manually from large texts is impractical.
Common use cases:
- Finding common phrases in documents
- Analyzing writing patterns and collocations
- Preparing data for natural language processing
Who benefits from this tool?
NLP practitioners analyzing text. Linguists studying phrase patterns. Content analysts finding common expressions.
Privacy first: All processing happens in your browser. Your text never leaves your device.
Frequently Asked Questions
Bigrams are two-word sequences (like "machine learning" or "of the"), while trigrams are three-word sequences (like "state of the" or "in order to"). Higher n-values capture longer phrases but appear less frequently.
N-grams reveal the multi-word phrases (long-tail keywords) naturally present in content. Analyzing top-ranking pages shows which phrases to target. They also help ensure your content uses natural phrase patterns that match user search queries.
It depends on your goal. Excluding stop words reveals meaningful topic phrases. Including them shows natural language patterns important for readability and language modeling. For SEO keyword research, exclude them; for linguistic analysis, keep them.
In short texts, even 2-3 occurrences may indicate a pattern worth examining. In longer documents or corpora, set higher thresholds (5+) to focus on truly common phrases. Statistical significance depends on text length and n-gram length.
N-grams power language models predicting the next word in sequences, text classification systems, spam filters, sentiment analysis, and machine translation. They capture local word context that single-word analysis misses.
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