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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 Paperback

Published in Aug 2014

Publisher

ISBN-13 9781782167853

Length 304 pages

Edition 2nd Edition

Languages

Python

Tools

NLTK

Concepts

Data Processing

Author (1):

Jacob Perkins

View More author details

Table of Contents (12) Chapters

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

Chapter 1. Tokenizing Text and WordNet Basics

In this chapter, we will cover the following recipes:

Tokenizing text into sentences
Tokenizing sentences into words
Tokenizing sentences using regular expressions
Training a sentence tokenizer
Filtering stopwords in a tokenized sentence
Looking up Synsets for a word in WordNet
Looking up lemmas and synonyms in WordNet
Calculating WordNet Synset similarity
Discovering word collocations

You have been reading a chapter from

Python 3 Text Processing with NLTK 3 Cookbook - Second Edition

Published in: Aug 2014

Publisher:

ISBN-13: 9781782167853

© 2014 Packt Publishing Limited All Rights Reserved

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Authors (1)

Jacob Perkins

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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