Raw text data requires significant preparation before it can be effectively used in machine learning models. Variations in spelling, punctuation, capitalization, and irrelevant characters can introduce noise and negatively impact performance. This chapter establishes the groundwork for processing text by introducing common preprocessing techniques used in Natural Language Processing.

You will learn about the typical stages of an NLP pipeline. We will examine various tokenization methods, including advanced subword techniques like Byte-Pair Encoding (BPE). We'll compare the effects of stemming and lemmatization, explore strategies for identifying and removing noise, customize stop word lists for specific needs, and apply text normalization procedures. The chapter concludes with a practical exercise where you'll implement these techniques to build a text preprocessing pipeline.