Book Image

Deep Learning for Natural Language Processing

By : Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu
Book Image

Deep Learning for Natural Language Processing

By: Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu

Overview of this book

Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing, but also be able to select the best text preprocessing and neural network models to solve a number of NLP issues.

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Table of Contents (11 chapters)
About the Book
About the Book
About the Authors
Description
Learning Objectives
Audience
Approach
Hardware Requirements
Software Requirements
Conventions
Installation and Setup
Install Python on Windows
Install Python on Linux
Install Python on macOS X
Installing Keras
Additional Resources
Free Chapter
1
Introduction to Natural Language Processing
Introduction to Natural Language Processing
Introduction
The Basics of Natural Language Processing
Capabilities of Natural language processing
Applications of Natural Language Processing
Word Embeddings
Summary
2
Applications of Natural Language Processing
Applications of Natural Language Processing
Introduction
POS Tagging
Applications of Parts of Speech Tagging
Chunking
Chinking
Named Entity Recognition
Summary
3
Introduction to Neural Networks
Introduction to Neural Networks
Introduction
Neural Networks
Training a Neural Network
Designing a Neural Network and Its Applications
Fundamentals of Deploying a Model as a Service
Summary
4
Foundations of Convolutional Neural Network
Foundations of Convolutional Neural Network
Introduction
Understanding the Architecture of a CNN
Training a CNN
Application Areas of CNNs
Summary
5
Recurrent Neural Networks
Recurrent Neural Networks
Introduction
Previous Versions of Neural Networks
RNNs
Updates and Gradient Flow
Gradients
Summary
6
Gated Recurrent Units (GRUs)
Gated Recurrent Units (GRUs)
Introduction
The Drawback of Simple RNNs
Gated Recurrent Units (GRUs)
Sentiment Analysis with GRU
Summary
7
Long Short-Term Memory (LSTM)
Long Short-Term Memory (LSTM)
Introduction
The Input Gate and the Candidate Cell State
Output Gate and Current Activation
Neural Language Translation
Summary
8
State-of-the-Art Natural Language Processing
State-of-the-Art Natural Language Processing
Introduction
Other Architectures and Developments
Activity 11: Build a Text Summarization Model
Summary
9
A Practical NLP Project Workflow in an Organization
A Practical NLP Project Workflow in an Organization
Introduction
Problem Definition
Data Acquisition
Google Colab
Flask
Deployment
Summary
Appendix
Appendix
Chapter 1: Introduction to Natural Language Processing
Chapter 2: Applications of Natural Language Processing
Chapter 3: Introduction to Neural Networks
Chapter 4: Introduction to convolutional networks
Chapter 5: Foundations of Recurrent Neural Network
Chapter 6: Foundations of GRUs
Chapter 7: Foundations of LSTM
Chapter 8: State of the art in Natural Language Processing
Chapter 9: A practical NLP project workflow in an organisation

Other Architectures and Developments

The attention mechanism architecture described in the last section is only a way of building attention mechanism. In recent times, several other architectures have been proposed, which constitute a state of the art in the deep learning NLP world. In this section, we will briefly mention some of these architectures.

Transformer

In late 2017, Google came up with an attention mechanism architecture in their seminal paper titled "Attention is all you need." This architecture is considered state-of-the-art in the NLP community. The transformer architecture makes use of a special multi-head attention mechanism to generate attention at various levels. Additionally, it is also employs residual connections to further ensure that the vanishing gradient problem has a minimal impact on learning. The special architecture of transformers also allows a massive speed up of the training phase while providing better quality results.

The most commonly used package...

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