Part 1 kicks off your natural language processing (NLP) adventure with an introduction to some real-world applications.
In chapter 1, you’ll quickly begin to think of ways you can use machines that process words in your own life. And hopefully you’ll get a sense for the magic—the power of machines that can glean information from the words in a natural language document. Words are the foundation of any language, whether it’s the keywords in a programming language or the natural language words you learned as a child.
In chapter 2, we give you the tools you need to teach machines to extract words from documents. There’s more to it than you might guess, and we show you all the tricks. You’ll learn how to automatically group natural language words together into groups of words with similar meanings without having to hand-craft synonym lists.
In chapter 3, we count those words and assemble them into vectors that represent the meaning of a document. You can use these vectors to represent the meaning of an entire document, whether it’s a 140-character tweet or a 500-page novel.
In chapter 4, you’ll discover some time-tested math tricks to compress your vectors down to much more useful topic vectors.
By the end of part 1, you’ll have the tools you need for many interesting NLP applications—from semantic search to chatbots.