Summary

In this chapter, we learned about how natural language processing enables humans and machines to communicate in natural human language. There are three broad applications of natural language processing, and these are speech recognition, natural language understanding, and natural language generation.

Language is a complicated thing, and so text is required to go through several phases before it can make sense to a machine. This process of filtering is known as text preprocessing and comprises various techniques that serve different purposes. They are all task- and corpora-dependent and prepare text for operations that will enable it to be input into machine learning and deep learning models.

Since machine learning and deep learning models work best with numerical data, it is necessary to transform preprocessed corpora into numerical form. This is where word embeddings come into the picture; they are real-value vector representations of words that aid models in predicting and understanding words. The two main algorithms used to generate word embeddings are Word2Vec and GloVe.

In the next chapter, we will be building on the algorithms used for natural language processing. The processes of POS tagging and named entity recognition will be introduced and explained.