Text Processing in NLP

Text Processing 

Text processing refers to cleaning and preparing raw text for NLP tasks. Since natural language is unstructured, we need to preprocess it to remove inconsistencies, noise, and unnecessary elements.

Steps in Text Processing

(a) Lowercasing

  • Converts all text to lowercase to maintain uniformity.
  • Example:
    • Before: "Natural Language Processing is Amazing!"
    • After: "natural language processing is amazing!"

(b) Removing Punctuation & Special Characters

  • Eliminates unnecessary symbols like !@#$%^&*().
  • Example:
    • Before: "Hello, how are you?"
    • After: "Hello how are you"

(c) Removing Stopwords

  • Stopwords are common words (e.g., the, is, in, at, which) that do not contribute much meaning.
  • Example:
    • Before: "The cat is sitting on the mat"
    • After: "cat sitting mat"

(d) Stemming vs. Lemmatization

  • Stemming: Reduces words to their root form (may not be a valid word).
    • Example: "running" → "run", "better" → "bet"
  • Lemmatization: Converts words to their dictionary form (uses linguistic rules).
    • Example: "running" → "run", "better" → "good"

(e) Removing Numbers

  • Some applications require removing numbers unless they carry meaning.
  • Example:
    • Before: "I have 2 cats and 3 dogs"
    • After: "I have cats and dogs"

(f) Handling Contractions

  • Expands contractions like "don't" to "do not" for better analysis.
  • Example:
    • Before: "I'll go"
    • After: "I will go"


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