Text Normalization

Text normalization is a preprocessing step that converts text into a standardized form so that equivalent expressions are treated identically. Common operations include converting to lowercase, expanding contractions, removing punctuation, normalizing whitespace, replacing numbers with words or tokens, and handling special characters.

Without normalization, the same word appearing in different forms — "Running", "running", "RUNNING" — may be treated as three distinct tokens, inflating vocabulary size and reducing statistical signal. Normalization reduces this sparsity and makes downstream models more robust.

The extent of normalization depends on the task. Aggressive normalization can improve recall in search and classification but may discard meaningful signals such as capitalization (which indicates proper nouns) or punctuation (which affects sentiment). Modern neural models with subword tokenization often apply lighter normalization than classical NLP pipelines, but the step remains relevant for rule-based systems, keyword search, and low-resource settings.