Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python 3 Text Processing with NLTK 3 Cookbook

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

Arrow left icon
Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Length 304 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Jacob Perkins Jacob Perkins
Author Profile Icon Jacob Perkins
Jacob Perkins
Arrow right icon
View More author details
Toc

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 classifier with NLTK-Trainer


In this recipe, we'll cover the train_classifier.py script from NLTK-Trainer, which lets you train NLTK classifiers from the command line. NLTK-Trainer was previously introduced at the end of Chapter 4, Part-of-speech Tagging, and again at the end of Chapter 5, Extracting Chunks.

Note

You can find NLTK-Trainer at https://github.com/japerk/nltk-trainer and the online documentation at http://nltk-trainer.readthedocs.org/.

How to do it...

Like train_tagger.py and train_chunker.py, the only required argument for train_classifier.py is the name of a corpus. The corpus must have a categories() method, because text classification is all about learning to classify categories. Here's an example of running train_classifier.py on the movie_reviews corpus:

$ python train_classifier.py movie_reviews
loading movie_reviews
2 labels: ['neg', 'pos']
using bag of words feature extraction
2000 training feats, 2000 testing feats
training NaiveBayes classifier
accuracy: 0...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime