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Python 3 Text Processing with NLTK 3 Cookbook

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

Product type Book

Published in Aug 2014

Publisher

ISBN-13 9781782167853

Pages 304 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Author (1):

Jacob Perkins

Table of Contents (17) Chapters

Python 3 Text Processing with NLTK 3 Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

1. Tokenizing Text and WordNet Basics

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

Penn Treebank Part-of-speech Tags

Index

Training a Brill tagger

The BrillTagger class is a transformation-based tagger. It is the first tagger that is not a subclass of SequentialBackoffTagger. Instead, the BrillTagger class uses a series of rules to correct the results of an initial tagger. These rules are scored based on how many errors they correct minus the number of new errors they produce.

How to do it...

Here's a function from tag_util.py that trains a BrillTagger class using BrillTaggerTrainer. It requires an initial_tagger and train_sents.

from nltk.tag import brill, brill_trainer

def train_brill_tagger(initial_tagger, train_sents, **kwargs):
  templates = [
    brill.Template(brill.Pos([-1])),
    brill.Template(brill.Pos([1])),
    brill.Template(brill.Pos([-2])),
    brill.Template(brill.Pos([2])),
    brill.Template(brill.Pos([-2, -1])),
    brill.Template(brill.Pos([1, 2])),
    brill.Template(brill.Pos([-3, -2, -1])),
    brill.Template(brill.Pos([1, 2, 3])),
    brill.Template(brill.Pos([-1]), brill.Pos([1])),
 ...

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Authors (1)

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.

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