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Deep Learning for Natural Language Processing

You're reading from   Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

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Product type Paperback
Published in Jun 2019
Publisher
ISBN-13 9781838550295
Length 372 pages
Edition 1st Edition
Languages
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Authors (4):
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Karthiek Reddy Bokka Karthiek Reddy Bokka
Author Profile Icon Karthiek Reddy Bokka
Karthiek Reddy Bokka
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
Tanuj Jain Tanuj Jain
Author Profile Icon Tanuj Jain
Tanuj Jain
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
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Toc

Table of Contents (11) Chapters Close

About the Book 1. Introduction to Natural Language Processing FREE CHAPTER 2. Applications of Natural Language Processing 3. Introduction to Neural Networks 4. Foundations of Convolutional Neural Network 5. Recurrent Neural Networks 6. Gated Recurrent Units (GRUs) 7. Long Short-Term Memory (LSTM) 8. State-of-the-Art Natural Language Processing 9. A Practical NLP Project Workflow in an Organization 1. Appendix

Chinking

Chinking is an extension of chunking, as you've probably guessed already from its name. It's not a mandatory step in processing natural language, but it can be beneficial.

Chinking is performed after chunking. Post chunking, you have chunks with their chunk tags, along with individual words with their POS tags. Often, these extra words are unnecessary. They don't contribute to the final result or the entire process of understanding natural language and thus are a nuisance. The process of chinking helps us deal with this issue by extracting the chunks, and their chunk tags form the tagged corpus, thus getting rid of the unnecessary bits. These useful chunks are called chinks once they have been extracted from the tagged corpus.

For example, if you need only the nouns or noun phrases from a corpus to answer questions such as "what is this corpus talking about?", you would apply chinking because it would extract just what you want and present it in front of your...

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