Lemmatize Text

The Lemmatize Text Tool reduces words in your input to their base forms. It standardizes text quickly, making it perfect for search indexing, data cleaning, or linguistic processing. Customize the output with a capitalization option and export your results instantly.

Paste your input above or import a file below.
No file chosen
Supported file type: text/plain (.txt)
Total words: 0
Options
Preserve Capitalization
Remove Stopwords
Custom Suffix Removal
Keep Only Unique Words

How to Use:

  1. Paste text into the Input Text box or import a .txt file.
  2. (Optional) Adjust Options:
    • Preserve Capitalization: Maintain original casing for proper nouns.
    • Remove Stopwords: Remove common filler words like “the”, “is”, “at”, etc.
    • Custom Suffix Removal: Remove preset suffixes such as “-ly”, “-ness”, “-ment”, and “-ous” from words.
    • Keep Only Unique Words: Remove duplicate words after processing.
  3. Click Lemmatize to instantly process your text.
  4. Copy or export the lemmatized output.
  5. Clear All to reset the tool for new input.

Feature Guide:

  • Built-in Lemmatization: Reduces words like “running” to “run” using a preset dictionary and suffix rules.
  • Stopword Removal: Eliminates common low-value words from the output.
  • Preset Suffix Removal: Automatically removes suffixes like “-ly”, “-ness”, “-ment”, and “-ous” to further simplify words.
  • Unique Words Option: Keeps only one instance of each word in the final output.
  • Live Word Counter: Updates in real time to show the total processed words.
  • File Import and Export: Load .txt files and download processed output.

Example:

Input Text:

The cats are running faster than the dogs, especially happily and carefully.

Options Enabled:

  • Remove Stopwords: ON
  • Custom Suffix Removal: ON
  • Keep Only Unique Words: ON

Output:

cat run fast dog especial happi careful

Total words: 7

Common Use Cases:

The Lemmatize Text Tool is ideal for creating standardized versions of large text datasets, improving search engine indexing, cleaning user-generated content, and preparing documents for natural language processing models. It speeds up text normalization, improves consistency, and reduces redundancy across projects.

Useful Tools & Suggestions:

After lemmatizing, Stem Words in Text can give you an even more reduced form if you’re doing deeper linguistic analysis. And if you’re planning to visualize or process the words further, Tokenize Text splits everything up so you can handle each term individually.

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