Mastering NLP from Foundations to LLMs

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Streamlining Text Preprocessing Techniques for Optimal NLP Performance

Text preprocessing stands as a vital initial step in the realm of natural language processing (NLP). It encompasses converting raw, unrefined text data into a format that machine learning algorithms can readily comprehend. To extract meaningful insights from textual data, it is essential to clean, normalize, and transform the data into a more structured form. This chapter provides an overview of the most commonly used text preprocessing techniques, including tokenization, stemming, lemmatization, stop word removal, and part-of-speech (POS) tagging, along with their advantages and limitations.

Effective text preprocessing is essential for various NLP tasks, including sentiment ...