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You're reading from  Python 3 Text Processing with NLTK 3 Cookbook

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Published inAug 2014
Reading LevelBeginner
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ISBN-139781782167853
Edition1st Edition
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Jacob Perkins
Jacob Perkins
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Jacob Perkins

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.
Read more about Jacob Perkins

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Chunking and chinking with regular expressions


Using modified regular expressions, we can define chunk patterns. These are patterns of part-of-speech tags that define what kinds of words make up a chunk. We can also define patterns for what kinds of words should not be in a chunk. These unchunked words are known as chinks.

A ChunkRule class specifies what to include in a chunk, while a ChinkRule class specifies what to exclude from a chunk. In other words, chunking creates chunks, while chinking breaks up those chunks.

Getting ready

We first need to know how to define chunk patterns. These are modified regular expressions designed to match sequences of part-of-speech tags. An individual tag is specified by surrounding angle brackets, such as <NN> to match a noun tag. Multiple tags can then be combined, as in <DT><NN> to match a determiner followed by a noun. Regular expression syntax can be used within the angle brackets to match individual tag patterns, so you can do <...

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Python 3 Text Processing with NLTK 3 Cookbook
Published in: Aug 2014Publisher: ISBN-13: 9781782167853

Author (1)

author image
Jacob Perkins

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.
Read more about Jacob Perkins