Summary
That was it—you made it to the end of this chapter! It was an exhaustive and long journey for sure, but we have unveiled the real linguistic power of spaCy to the fullest. This chapter gave you details of spaCy's linguistic features and how to use them.
You learned about POS tagging and applications, with many examples. You also learned about an important yet not so well-known and well-used feature of spaCy—the dependency labels. Then, we discovered a famous NLU tool and concept, NER. We saw how to do named entity extraction, again via examples. We finalized this chapter with a very handy tool for merging and splitting the spans that we calculated in the previous sections.
What's next, then? In the next chapter, we will again be discovering a spaCy feature that you'll be using every day in your NLP application code—spaCy's Matcher class. We don't want to give a spoiler on this beautiful subject, so let's go onto our journey...
