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Hands-On Natural Language Processing with Python

You're reading from Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781789139495

Length 312 pages

Edition 1st Edition

Languages

Processing

Tools

NLTK

Concepts

Deep Learning

Authors (5):

Shanmugamani

Arumugam

Byiringiro

Joshi

Muthuswamy

+1 more

View More author details

Table of Contents (20) Chapters

Title Page

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started

2. Text Classification and POS Tagging Using NLTK FREE CHAPTER

3. Deep Learning and TensorFlow

4. Semantic Embedding Using Shallow Models

5. Text Classification Using LSTM

6. Searching and DeDuplicating Using CNNs

7. Named Entity Recognition Using Character LSTM

8. Text Generation and Summarization Using GRUs

9. Question-Answering and Chatbots Using Memory Networks

10. Machine Translation Using the Attention-Based Model

11. Speech Recognition Using DeepSpeech

12. Text-to-Speech Using Tacotron

13. Deploying Trained Models

1. Other Books You May Enjoy

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Index

Overview of machine translation

There are different types of machine translation methods that are in use, but for conciseness, we will look into two of the main approaches. One of them is statistical machine translation (SMT) and the other is neural machine translation (NMT), which is the topic of this chapter. We will briefly look at these two methods.

Statistical machine translation

Statistical machine translation combines a translation model with a target language model to convert sentences from the source text in one language to sentences in the target language. This is illustrated in the following diagram. The translation model maps words and phrases from the source language to the target language. The language model captures statistics of how likely words follow a specific sequence in the target language. SMT, therefore, tries to maximize the probability of choosing a target sentence that is the translation of the source sentence. These statistical models are derived from a large corpus...

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Authors (5)

Shanmugamani

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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Arumugam

Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.

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Byiringiro

Byiringiro

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Joshi

Joshi

See other products by Joshi

Muthuswamy

Muthuswamy

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