N-gram Extractor
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Extract word sequences (bigrams, trigrams, etc.) from text.
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
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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