Summary
In this chapter, we saw how to transform text to an increasingly complex vector representation. This numerical representation of text allowed us to be able to use machine learning models. We saw how to preserve the contextual information (word embedding) of a text and how this can then be used for later analysis (for example, searching synonyms or clustering words). In addition, we saw how neural networks (RNNs, LSTM, GRUs) can be used to analyze text and perform tasks (for example, sentiment analysis).
In the next chapter, we will see how to solve some of the remaining unsolved challenges and see how this will lead to the natural evolution of the models seen here.
