Summary
In this chapter, we surveyed the various techniques that can be used in NLU applications and learned several important skills.
We learned about what rule-based approaches are and the major rule-based techniques, including topics such as POS tagging and parsing. We then learned about the important traditional machine learning techniques, especially the ways that text documents can be represented numerically. Next, we focused on the benefits and drawbacks of the more modern deep learning techniques and the advantages of pre-trained models.
In the next chapter, we will review the basics of getting started with NLU – installing Python, using Jupyter Labs and GitHub, using NLU libraries such as NLTK and spaCy, and how to choose between libraries.
