We moved beyond the basic preprocessing steps in this chapter. We looked deeper at NLP techniques, such as parsing and information extraction. We discussed parsing in detail, which parsers are available, and how to use NLTK to do any NLP parsing. You understood the concept of CFG and PCFG and how to learn from a tree bank and build a parser. We talked about shallow and deep parsing and what the difference is between them.

We also talked about some of the information extraction essentials, such as entity extraction and relation extraction. We talked about a typical information extraction engine pipeline. We saw a very small and simple IE engine that can be built in less than 100 lines of code. Think about this kind of system running on an entire Wikipedia dump or an entire web content related to an organization. Cool, isn't it?

We will use some of the topics we've learnt in this chapter in further chapters to build some useful NLP applications.