Description: Text normalization is the process of converting text into a standard format, which involves a series of transformations aimed at simplifying and unifying the representation of textual data. This process is fundamental in data preprocessing and natural language processing (NLP), as it allows algorithms and text analysis models to operate more efficiently and effectively. Normalization can include converting text to lowercase, removing special characters, correcting typographical errors, eliminating unnecessary whitespace, and lemmatization or stemming, which are techniques for reducing words to their base form. By standardizing text, it facilitates comparison and analysis, resulting in improved quality of outcomes in tasks such as text classification, sentiment analysis, and information extraction. Therefore, text normalization is a crucial step in preparing data for any application involving natural language processing, as it helps reduce the complexity and variability of human language, allowing models to learn clearer and more precise patterns.
