Text Normalisation in NLP

Text normalisation transforms diverse text formats into a consistent, standard form essential for accurate language processing. It encompasses techniques such as case folding, stemming, lemmatisation, spelling correction, punctuation removal, and whitespace cleaning. These steps reduce data sparsity, correct inconsistencies, and enhance computational interpretation. By ensuring semantically similar expressions are treated uniformly, normalisation improves the performance of downstream NLP tasks such as information retrieval, sentiment analysis, and text classification, serving as a critical foundation for high-quality textual analysis.