Text Preprocessing Techniques

Text Preprocessing for NLP

Why Preprocessing Matters

Raw text is noisy and inconsistent. Preprocessing creates clean, uniform input for models.

Common Techniques

1. Lowercasing "Hello World" → "hello world"

2. Tokenization Breaking text into tokens (words, subwords, characters)

3. Removing Punctuation & Special Characters Keep or remove based on task

4. Stopword Removal Remove common words: "the", "is", "at" (Not always useful for modern NLP)

5. Stemming & Lemmatization

Modern Approach

For transformer-based models: