Natural Language Processing with Python

Organization

The early chapters are organized in order of conceptual difficulty, starting with a practical introduction to language processing that shows how to explore interesting bodies of text using tiny Python programs (Chapters 1–3). This is followed by a chapter on structured programming (Chapter 4) that consolidates the programming topics scattered across the preceding chapters. After this, the pace picks up, and we move on to a series of chapters covering fundamental topics in language processing: tagging, classification, and information extraction (Chapters 5–7). The next three chapters look at ways to parse a sentence, recognize its syntactic structure, and construct representations of meaning (Chapters 8–10). The final chapter is devoted to linguistic data and how it can be managed effectively (Chapter 11). The book concludes with an Afterword, briefly discussing the past and future of the field.

Within each chapter, we switch between different styles of presentation. In one style, natural language is the driver. We analyze language, explore linguistic concepts, and use programming examples to support the discussion. We often employ Python constructs that have not been introduced systematically, so you can see their purpose before delving into the details of how and why they work. This is just like learning idiomatic expressions in a foreign language: you’re able to buy a nice pastry without first having learned the intricacies of question formation. In the other style ...