This is a recipe-based Cookbook, where each chapter is divided into individual recipes, each presenting a new concept, technique or algorithm. Many of the recipes build upon or make use of techniques from previous recipes and chapters, so it is recommend that you start from the beginning, and do not skip ahead, unless you re already familiar with the previous content.
This book is intended for Python programmers interested in learning how to do natural language processing. Maybe you ve learned the limits of regular expressions the hard way, or you ve realized that human language cannot be deterministically parsed like a computer language. Perhaps you have more text than you know what to do with, and need automated ways to analyze and structure that text. This Cookbook will show you how to train and use statistical language models to process text in ways that are practically impossible with standard programming tools. A basic knowledge of Python and the basic text processing concepts is expected. Some experience with regular expressions will also be helpful.

About the Author

Jacob Perkins is the cofounder and CTO of Weotta, a local search company. Weotta uses NLP and machine learning to create powerful and easy-to-use natural language search for what to do and where to go. He is the author of Python Text Processing with NLTK 2.0 Cookbook, Packt Publishing, and has contributed a chapter to the Bad Data Handbook, O'Reilly Media. He writes about NLTK, Python, and other technology topics at http: //streamhacker.com. To demonstrate the capabilities of NLTK and natural language processing, he developed http: //text-processing.com, which provides simple demos and NLP APIs for commercial use. He has contributed to various open source projects, including NLTK, and created NLTK-Trainer to simplify the process of training NLTK models. For more information, visit https: //github.com/japerk/nltk-trainer.

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