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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

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788478311

Length 472 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (2):

Saad

Ganegedara

View More author details

Table of Contents (16) Chapters

Natural Language Processing with TensorFlow

Contributors

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

Mathematical Foundations and Advanced TensorFlow

Index

Current trends in NLP

In this section, we will talk about current trends in NLP. These trends are from the NLP research conducted between 2012 and early 2018. First let's talk about the current states of word embeddings. Word embeddings is a crucial topic as we have already seen many interesting tasks that rely on word embeddings to perform well. We will then look at important improvements in NMT.

Word embeddings

Many variants of word embeddings have emerged over time. With the inception of high-quality word embeddings (refer to Distributed representations of words and phrases and their compositionality, Mikolov and others [1]) in NLP, it can be said that NLP had a resurgence, where many took an interest in using word embeddings in various NLP tasks (for example, sentiment analysis, machine translation, and question answering). Also, there have been many attempts to improve word embeddings, leading to even better embeddings. The four models that we'll introduce are in the areas of region embedding...

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Authors (2)

Saad

Saad

See other products by Saad

Ganegedara

Ganegedara

Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.

See other products by Ganegedara

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