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Python 3 Text Processing with NLTK 3 Cookbook Paperback – August 26, 2014
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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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Top customer reviews
This book pales in comparison in communication, content, and utility as it relates to both NLTK and Python (in general) - you don't even get a table of contents.
A lot of content provided without proper resources.
Many of his red URL or package are out dated and not useful at all.
Very irresponsible author- i contact author about many issues he never ever answered
The book is intended for those familiar with Python who want to use it in order to process natural language. Following this credo, there is no discussion about software design and no attempt to make especially elegant code. I tend to nitpick at code quality, and although there was nothing that upset me in the code examples here, they didn't awe me with their subtle beauty. However, the raw power of NLTK, combined with the flexibility of Python, impressed me deeply.
The author takes you on a trip through a large section of natural language processing, starting with text tokenization and using Wordnet. I really enjoyed ideas on computing the semantic "distance" between different words by traversing subset trees. It then continues on to show you how to replace and correct words, tag parts of speech intexts, chunk texts and transform text chunks, and how to classify text. The whole thing is rounded off by a discussion on distributed processing with some nice examples of how to use execnet as a simple but effective message passing interface.
Reading all these examples made me want to go out and write a search engine or a text classifier - with NLTK, daunting tasks in this field become easy.
Above and beyond the practical text processing material in this book, what I enjoyed most was its coverage of various machine learning algorithms. The book definitely is not about machine learning, but it affords you a glimpse into the world of machine learning in a way that you can understand what you're doing if you're just using what different libraries give you out of the box. I appreciated these more extended explanations, which I often miss in texts involving machine learning.
So, if you want to know how to adopt NLTK, Wordnet, Scipy, Numpy, and the like to the problems you are facing right now this is your book. It will have you up and running in hours, not weeks, with plenty of code recipes included. The author also maintains an excellent blog, streamhacker.com, if you want to get a sense for his knowledge and writing style before buying. It's how I found this book.
An excellent next book, if you need a more complete book to build your own fundamental tools, rather than simply adopting NLTK, is Fundamentals of Predictive Text Mining by Weiss. Fundamentals will take you from the launch point provided by this book into computational and predictive methods.
Author is highly lucid and consistent in his explanation; He leaves nothing to chance and,when required, He delves into the topic.
Material is extremely useful, code is very well designed! (I have read many texts on Python where code was catastrophic…) ; Excellent Support.