Text Tokenizer & Stemmer

Overview

The Text Tokenizer & Stemmer breaks text into tokens (words) and reduces them to base forms. Explore different tokenization strategies (whitespace, punctuation, regex), stemming algorithms (Porter, Snowball), and see token statistics. Essential for text preprocessing in NLP pipelines.

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Tips

• Tokenization splits text into meaningful units (words, sentences)
• Stemming reduces words to root forms (running → run, studies → studi)
• Porter stemmer is most common for English
• Stopword removal filters common words (the, is, at)
• Try different tokenization methods to see effects on token count
• Applications: search engines, text classification, sentiment analysis
• Case normalization (lowercasing) improves matching