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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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Toc

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

Summary


This chapter was aimed at learning the current trends in NLP and learning the future directions that NLP is being driven to. Though it is a very broad topic, we discussed some of the very recent advancements that have been made in NLP. As current trends, we first looked at the advancements being made with regard to word embeddings. We saw that much more accurate embeddings with richer interpretations (for example, probabilistic) are emerging. Then we looked into improvements that have been made in machine translation, as it is one of the most sought after areas in NLP. We saw that better attention mechanisms and better MT models capable of producing increasingly more realistic translations are both emerging.

We then looked at some of the novel research in NLP that is taking place (mostly in 2017). First we investigated the penetration of NLP into other fields: computer vision, reinforcement learning, and the generative adversarial models. We looked at how NLP systems are being improved...

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