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Python 3 Text Processing with NLTK 3 Cookbook

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

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Length 304 pages
Edition 2nd Edition
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Author (1):
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Jacob Perkins Jacob Perkins
Author Profile Icon Jacob Perkins
Jacob Perkins
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Table of Contents (12) Chapters Close

Preface 1. Tokenizing Text and WordNet Basics FREE CHAPTER 2. Replacing and Correcting Words 3. Creating Custom Corpora 4. Part-of-speech Tagging 5. Extracting Chunks 6. Transforming Chunks and Trees 7. Text Classification 8. Distributed Processing and Handling Large Datasets 9. Parsing Specific Data Types A. Penn Treebank Part-of-speech Tags
Index

Training a tagger-based chunker


Training a chunker can be a great alternative to manually specifying regular expression chunk patterns. Instead of a pain-staking process of trial and error to get the exact right patterns, we can use existing corpus data to train chunkers much like we did for part-of-speech tagging in the previous chapter.

How to do it...

As with the part-of-speech tagging, we'll use the treebank corpus data for training. But this time, we'll use the treebank_chunk corpus, which is specifically formatted to produce chunked sentences in the form of trees. These chunked_sents() methods will be used by a TagChunker class to train a tagger-based chunker. The TagChunker class uses a helper function, conll_tag_chunks(), to extract a list of (pos, iob) tuples from a list of Trees. These (pos, iob) tuples are then used to train a tagger in the same way (word, pos) tuples were used in Chapter 4, Part-of-speech Tagging, to train part-of-speech taggers. But instead of learning part-of...

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