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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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Creating a part-of-speech tagged word corpus


Part-of-speech tagging is the process of identifying the part-of-speech tag for a word. Most of the time, a tagger must first be trained on a training corpus. How to train and use a tagger is covered in detail in Chapter 4, Part-of-speech Tagging, but first we must know how to create and use a training corpus of part-of-speech tagged words.

Getting ready

The simplest format for a tagged corpus is of the form word/tag. The following is an excerpt from the brown corpus:

The/at-tl expense/nn and/cc time/nn involved/vbn are/ber astronomical/jj ./.

Each word has a tag denoting its part-of-speech. For example, nn refers to a noun, while a tag that starts with vb is a verb.

Note

Different corpora can use different tags to mean the same thing. For example, the treebank corpus uses different tags as compared to the brown corpus, even though both are English text. But both sets of tags can be converted into a universal tagset, described at the end of this recipe...

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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