Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Natural Language Processing with TensorFlow

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

Arrow left icon
Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 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

Machine translation


Humans often communicate with each other by means of a language, compared to other communication methods (for example, gesturing). Currently, more than 5,000 languages are spoken worldwide. Furthermore, learning a language to a level where it is easily understandable for 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 for communicating with different parts of the world. This is where machine translation (MT) comes in. MT systems allow the user to input a sentence in his 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) belonging to a source language S, defined by the following:

Here, .

The source language would be translated to a sentence , where T is the target language and...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £16.99/month. Cancel anytime