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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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Extracting proper noun chunks


A simple way to do named entity extraction is to chunk all proper nouns (tagged with NNP). We can tag these chunks as NAME, since the definition of a proper noun is the name of a person, place, or thing.

How to do it...

Using the RegexpParser class, we can create a very simple grammar that combines all proper nouns into a NAME chunk. Then, we can test this on the first tagged sentence of treebank_chunk to compare the results with the previous recipe:

>>> chunker = RegexpParser(r'''
... NAME:
...   {<NNP>+}
... ''')
>>> sub_leaves(chunker.parse(treebank_chunk.tagged_sents()[0]), 'NAME')
[[('Pierre', 'NNP'), ('Vinken', 'NNP')], [('Nov.', 'NNP')]]

Although we get Nov. as a NAME chunk, this isn't a wrong result, as Nov. is the name of a month.

How it works...

The NAME chunker is a simple usage of the RegexpParser class, covered in the Chunking and chinking with regular expressions, Merging and splitting chunks with regular expressions, and Partial...

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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