Text preprocessing in NLP projects involves techniques like tokenization, stemming, and stop word removal to transform raw text data into a format that can be understood by NLP models. Tokenization is the process of breaking up text into smaller chunks or tokens, such as words, sentences, or n-grams. Choosing the right method for tokenization is important for accurate results and better performance in NLP tasks.
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