Book Image

Cognitive Computing with IBM Watson

By : Rob High, Tanmay Bakshi
Book Image

Cognitive Computing with IBM Watson

By: Rob High, Tanmay Bakshi

Overview of this book

Cognitive computing is rapidly becoming a part of every aspect of our lives through data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system increases. This book introduces you to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI with the help of IBM Watson APIs. This book will help you build your own applications to understand, and solve problems, and analyze them as per your needs. You will explore various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems. Equipped with the knowledge of machine learning concepts, how computers do their magic, and the applications of these concepts, you’ll be able to research and apply cognitive computing in your projects.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
About Packt

Chapter 6. Language - How Watson Deals with NL

In this chapter, you're going to learn the inner workings of one of the most complex tasks for machine learning today: language translation! Once you learn the inner workings, you'll be introduced to Watson's Language Translator service, and how Watson's complex technology is wrapped into just a few lines of code for you. Then, you'll be shown how to use the Natural Language Classifier service, which enables you to categorize text into different categories, much like the Visual Recognition service, but for text.

Natural Language Translation is specifically a very difficult task. Let's explore how it's been done in the past!

In this chapter, we will cover the following topics:

  • Translating natural languages – the past
  • Translating natural languages – the present
  • Translating between languages with language translator
  • Training custom NMT models with Watson
  • Categorizing text using natural language classifier