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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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Toc

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

Machine translation

Humans often communicate with each other by means of a language, compared to other communication methods (for example, gesturing). Currently, more than 6,000 languages are spoken worldwide. Furthermore, learning a language to a level where it is easily understandable to a native speaker of that language is a difficult task to master. However, communication is essential for sharing knowledge, socializing, and expanding your network. Therefore, language acts as a barrier to communicating with people in different parts of the world. This is where Machine Translation (MT) comes in. MT systems allow the user to input a sentence in their own tongue (known as the source language) and output a sentence in a desired target language.

The problem with MT can be formulated as follows. Say we are given a sentence (or a sequence of words) Ws belonging to a source language S, defined by the following:

Here, .

The source language would be translated to a sentence...

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