Building Cognitive Applications with IBM Watson Services: Volume 4 Natural Language Classifier

Building Cognitive Applications with IBM Watson Services: Volume 4 Natural Language Classifier

by Marcelo Mota Manhaes, Taemin Ko, Abeer Selim, Omar Amer, Lak Sri
Released May 2017
Publisher(s): IBM Redbooks
ISBN: 9780738442594

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Book description

The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® Watson™ cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases.

The series includes the following volumes:


  • Volume 1 Getting Started, SG24-8387
  • Volume 2 Conversation, SG24-8394
  • Volume 3 Visual Recognition, SG24-8393
  • Volume 4 Natural Language Classifier, SG24-8391
  • Volume 5 Language Translator, SG24-8392
  • Volume 6 Speech to Text and Text to Speech, SG24-8388
  • Volume 7 Natural Language Understanding, SG24-8398

Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool.

This IBM Redbooks® publication, Volume 4, introduces the Watson Natural Language Classifier service. This service applies cognitive computing techniques to return best matching predefined classes for short text inputs such as a sentence or phrase. The book describes concepts that you need to understand to create, use and train the classifier. This book describes how to prepare training data, and create and train the classifier to connect the classes to example texts so the service can apply the classes to new inputs. It provides examples of applications that demonstrate how to use the Watson Natural Language Classifier service in practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application.

Table of contents

  1. Front cover
  2. Notices
    1. Trademarks
  3. Preface
    1. Authors
    2. Now you can become a published author, too!
    3. Comments welcome
    4. Stay connected to IBM Redbooks
  4. Chapter 1. Basics of Natural Language Classifier service
    1. 1.1 Using the Natural Language Classifier service
      1. 1.1.1 Prepare training data
      2. 1.1.2 Create and train the classifier
      3. 1.1.3 Query the trained classifier
      4. 1.1.4 Evaluate results and update the data
    2. 1.2 References
  5. Chapter 2. Creating a Natural Language Classifier service in Bluemix
    1. 2.1 Requirements
    2. 2.2 Creating the Natural Language Classifier service instance
      1. 2.2.1 Creating the Natural Language Classifier service instance from theBluemix website
      2. 2.2.2 Creating the Natural Language Classifier service instance using Cloud Foundry commands
    3. 2.3 What to do next
  6. Chapter 3. Healthcare questions and answers
    1. 3.1 Getting started
      1. 3.1.1 Objectives
      2. 3.1.2 Prerequisites
      3. 3.1.3 Expected results
    2. 3.2 Architecture
    3. 3.3 Two ways to deploy the application: Step-by-step and quick deploy
    4. 3.4 Step-by-step implementation
      1. 3.4.1 Downloading the project from Git
      2. 3.4.2 Preparing training data
      3. 3.4.3 Creating and training the classifier
      4. 3.4.4 Creating the Node.js Express Healthcare Q and A application
      5. 3.4.5 Deploying the Healthcare Q and A application on Bluemix
      6. 3.4.6 Testing the application
    5. 3.5 Quick deployment of application
    6. 3.6 References
  7. Chapter 4. News Classification
    1. 4.1 Getting started
      1. 4.1.1 Objectives
      2. 4.1.2 Prerequisites
      3. 4.1.3 Expected results
    2. 4.2 Architecture
    3. 4.3 Two ways to deploy the application: Step-by-step and quick deploy
    4. 4.4 Step-by-step implementation
      1. 4.4.1 Downloading the project from Git
      2. 4.4.2 Reviewing the project structure
      3. 4.4.3 Creating a Cloudant noSQL DB service instance
      4. 4.4.4 Preparing training data
      5. 4.4.5 Creating and training the classifier
      6. 4.4.6 Querying the trained classifier
      7. 4.4.7 Evaluating results and updating training data
      8. 4.4.8 Deploying the application
      9. 4.4.9 Testing the application
    5. 4.5 Quick deployment of application
    6. 4.6 References
  8. Chapter 5. SPAM Classifier
    1. 5.1 Getting started
      1. 5.1.1 Objectives
      2. 5.1.2 Prerequisites
      3. 5.1.3 Expected results
    2. 5.2 Architecture
      1. 5.2.1 Component perspective
      2. 5.2.2 Role and activity perspective
    3. 5.3 Two ways to deploy the application: Step-by-step and quick deploy
    4. 5.4 Step-by-step implementation
      1. 5.4.1 Creating a Node-RED application
      2. 5.4.2 Cloning the Git project
      3. 5.4.3 Preparing training data
      4. 5.4.4 Creating and training the classifier
      5. 5.4.5 Querying the trained classifier
      6. 5.4.6 Evaluating results and updating training data
    5. 5.5 Quick deployment of application
    6. 5.6 References
  9. Appendix A. Additional material
    1. Locating the web material
  10. Related publications
    1. IBM Redbooks
    2. Online resources
    3. Help from IBM
  11. Back cover

Product information

  • Title: Building Cognitive Applications with IBM Watson Services: Volume 4 Natural Language Classifier
  • Author(s): Marcelo Mota Manhaes, Taemin Ko, Abeer Selim, Omar Amer, Lak Sri
  • Release date: May 2017
  • Publisher(s): IBM Redbooks
  • ISBN: 9780738442594