The field of NLP is vast and complex. Any interaction between human language and computer science might technically fall into this category. For the sake of this discussion though, I'll confine NLP to analyzing, understanding, and, sometimes, generating human language.

From the beginnings of computer science, we've been fascinated by NLP as a gateway to strong artificial intelligence. In 1950, Alan Turing proposed the Turing test, which involves a computer impersonating a human so well that it's indistinguishable from another human, as a metric for machine intelligence. Ever since, we've worked to find clever ways to help machines understand human language. Along the way, we've developed speech-to-text transcription, automatic translation between human languages, the automatic summation of documents, topic modeling, named entity identification, and a variety of other use cases.

As our understanding of NLP continues to grow, we find AI applications becoming common in everyday life. Chatbots have become commonplace as customer service applications and, more recently, they have become our personal digital assistants. As I write this, I'm able to ask Alexa to add something to my shopping list or play some smooth jazz. Natural language processing connects humans to computers in a very interesting and powerful way.

In this chapter, I'm going to focus on understanding human language and then using that understanding to classify. I will actually have two classification case studies, one that covers semantic analysis and another that covers document classification. Both case studies provide great opportunities for the application of deep learning, and they're really very similar.