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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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Distributed chunking with execnet


In this recipe, we'll do chunking and tagging over an execnet gateway. This will be very similar to the tagging in the previous recipe, but we'll be sending two objects instead of one, and we will be receiving a Tree instead of a list, which requires pickling and unpickling for serialization.

Getting ready

As in the previous recipe, you must have execnet installed.

How to do it...

The setup code is very similar to the last recipe, and we'll use the same pickled tagger as well. First, we'll pickle the default chunker used by nltk.chunk.ne_chunk(), though any chunker would do. Next, we make a gateway for the remote_chunk module, get a channel, and send the pickled tagger and chunker over. Then, we receive a pickled Tree, which we can unpickle and inspect to see the result. Finally, we exit the gateway:

>>> import execnet, remote_chunk
>>> import nltk.data, nltk.tag, nltk.chunk
>>> import pickle
>>> from nltk.corpus import treebank_chunk...
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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