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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

The Drawback of Simple RNNs

Let's take a look at a simple example in order to revisit the concept of vanishing gradients.

Essentially, you wish to generate an English poem using an RNN. Here, you set up a simple RNN to do your bidding and it ends up producing the following sentence:

"The flowers, despite it being autumn, blooms like a star".

One can easily spot the grammatical error here. The word 'blooms' should be 'bloom' since at the beginning of the sentence, the word 'flowers' indicates that you should be using the plural form of the word 'bloom' to bring about the subject-verb agreement in the sentence. A simple RNN fails at this job because it is incapable of retaining any information about a dependency between the word 'flowers' that occurs early in the sentence and the word 'blooms,' which occurs much later (theoretically, it should be able to!).

A GRU helps to solve this issue by eliminating the 'vanishing...

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