Summary
In this chapter, we had a look at text preprocessing, which is an essential step in NLP. We saw different text cleaning techniques, from handling HTML tags and capitalization to addressing numerical values and whitespace challenges. We deep-dived into tokenization, examining word and subword tokenization, with practical Python examples. Finally, we explored various methods for embedding documents and introduced some of the most popular embedding models available today.
In the next chapter, we will continue our journey with unstructured data, delving into image and audio preprocessing.
