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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Chapter 4. Advanced Word2vec

In Chapter 3, Word2vec – Learning Word Embeddings, we introduced you to Word2vec, the basics of learning word embeddings, and the two common Word2vec algorithms: skip-gram and CBOW. In this chapter, we will discuss several topics related to Word2vec, focusing on these two algorithms and extensions.

First, we will explore how the original skip-gram algorithm was implemented and how it compares to its more modern variant, which we used in Chapter 3, Word2vec – Learning Word Embeddings. We will examine the differences between skip-gram and CBOW and look at the behavior of the loss over time of the two approaches. We will also discuss which method works better, using both our observation and the available literature.

We will discuss several extensions to the existing Word2vec methods that boost performance. These extensions include using more effective sampling techniques to sample negative examples for negative sampling and ignoring uninformative...

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