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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

Introduction

In this chapter, we'll cover how to use corpus readers and create custom corpora. If you want to train your own model, such as a part-of-speech tagger or text classifier, you will need to create a custom corpus to train on. Model training is covered in the subsequent chapters.

Now you'll learn how to use the existing corpus data that comes with NLTK. This information is essential for future chapters when we'll need to access the corpora as training data. You've already accessed the WordNet corpus in Chapter 1, Tokenizing Text and WordNet Basics. This chapter will introduce you to many more corpora.

We'll also cover creating custom corpus readers, which can be used when your corpus is not in a file format that NLTK already recognizes, or if your corpus is not located in files at all, but instead is located in a database such as MongoDB. It is essential to be familiar with tokenization, which was covered in Chapter 1, Tokenizing Text and WordNet Basics...

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