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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781838641351
Length 514 pages
Edition 2nd Edition
Languages
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Author (1):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 2 3. Word2vec – Learning Word Embeddings 4. Advanced Word Vector Algorithms 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Understanding Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Sequence-to-Sequence Learning – Neural Machine Translation 10. Transformers 11. Image Captioning with Transformers 12. Other Books You May Enjoy
13. Index
Appendix A: Mathematical Foundations and Advanced TensorFlow

Using CNNs for sentence classification

Though CNNs have mostly been used for computer vision tasks, nothing stops them from being used in NLP applications. But as we highlighted earlier, CNNs were originally designed for visual content. Therefore, using CNNs for NLP tasks requires somewhat more effort. This is why we started out learning about CNNs with a simple computer vision problem. CNNs are an attractive choice for machine learning problems due to the low parameter count of convolution layers. One such NLP application for which CNNs have been used effectively is sentence classification.

In sentence classification, a given sentence should be classified with a class. We will use a question database, where each question is labeled by what the question is about. For example, the question “Who was Abraham Lincoln?” will be a question and its label will be Person. For this we will use a sentence classification dataset available at http://cogcomp.org/Data/QA/QC/; here...

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