NLTK is great for in-memory, single-processor natural language processing. However, there are times when you have a lot of data to process and want to take advantage of multiple CPUs, multicore CPUs, and even multiple computers. Or, you might want to store frequencies and probabilities in a persistent, shared database so multiple processes can access it simultaneously. For the first case, we'll be using execnet to do parallel and distributed processing with NLTK. For the second case, you'll learn how to use the Redis data structure server/database to store frequency distributions and more.
Python 3 Text Processing with NLTK 3 Cookbook
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Python 3 Text Processing with NLTK 3 Cookbook
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Overview of this book
Table of Contents (17 chapters)
Python 3 Text Processing with NLTK 3 Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Tokenizing Text and WordNet Basics
Replacing and Correcting Words
Creating Custom Corpora
Part-of-speech Tagging
Extracting Chunks
Transforming Chunks and Trees
Text Classification
Distributed Processing and Handling Large Datasets
Parsing Specific Data Types
Penn Treebank Part-of-speech Tags
Index
Customer Reviews