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.
Table of Contents (11 chapters)

Capabilities of Natural language processing

Natural language processing has many real-world applications that benefit the lives of humans. These applications fall under three broad capabilities of natural language processing:

  • Speech Recognition

    The machine is able to recognize a natural language in its spoken form and translate it into a textual form. An example of this is dictation on your smartphones – you can enable dictation and speak to your phone, and it will convert whatever you are saying into text.

  • Natural Language Understanding

    The machine is able to understand a natural language in both its spoken and written form. If given a command, the machine is able to understand and execute it. An example of this would be saying 'Hey Siri, call home' to Siri on your iPhone for Siri to automatically call 'home' for you.

  • Natural Language Generation

    The machine is able to generate natural language itself. An example of this is asking 'Siri, what time is it?' to Siri on your iPhone and Siri replying with the time – 'It's 2:08pm'.

These three capabilities are used to accomplish and automate a lot of tasks. Let's take a look at some of the things natural language processing contributes to, and how.

Note

Textual data is known as corpora (plural) and a corpus (singular).