Generate Text Skip-grams

The Generate Text Skip-grams Tool lets you build flexible word pairings with skips between words, enabling deep text analysis beyond direct neighbors. Unlike traditional bigrams, skip-grams include pairs that are separated by up to a user-defined number of words, making them incredibly useful for linguistic research and NLP feature engineering.

This tool is perfect for extracting co-occurrence patterns, training skip-gram word embeddings, or analyzing sentence structure with a wider context. Input text manually or import from common formats like .txt, .csv, or .json. Configure max skips, normalize text, and export results instantly.

Total items: 0
Options
Convert to lowercase
Remove punctuation
Sort output alphabetically

How to Use:

  1. Paste your text or import a file using the button below the input box.
  2. Set the Max Skip to define how far apart paired words can be.
  3. Use toggles to remove punctuation, convert to lowercase, or sort results.
  4. View the live output in the second box and export it when ready.

What Generate Text Skip-grams Tool can do:

It builds word pairs that are not only next to each other but also a few words apart, allowing you to capture more flexible and meaningful connections. This is especially useful for word2vec, semantic modeling, and pattern detection in complex texts.

Example:

Input:

The quick brown fox jumps

Max Skip = 2

Output:

The quick
The brown
The fox
quick brown
quick fox
quick jumps
brown fox
brown jumps
fox jumps

Common Use Cases:

  • Preprocessing data for skip-gram-based neural networks
  • Studying co-occurrence frequency in corpora
  • Enriching text classification features
  • Creating flexible keyword pair lists for SEO or clustering
  • Analyzing dialogue, poetry, or literature with word-distance flexibility

The Generate Text Skip-grams Tool gives you fast, exportable insight into relationships between words in your text all in your browser, with no data uploads.

Useful Tools & Suggestions:

If you’re experimenting with linguistic patterns, Generate Text N-grams gives you more traditional adjacent sequences to compare against. And when you’re building datasets or models, Generate Text Bigrams offers a simpler structure that still captures useful connections between words.

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