In four videos, you can learn about the Watson Natural Language Understanding service and discover how to use it to analyze text data from your application.

In this video:

  • Joshua Elliot, Software Engineer, IBM Watson Natural Language Understanding

The Watson Natural Language Understanding service allows you to add the ability to your application to let you perform natural language processing that can enable advanced text analysis capabilities. (Natural language processing is the field of study concerned with programming computers to successfully process large samples of actual human language to derive a set of rules that govern the natural language in question.)

The service offers developers the ability to analyze text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles, all using natural language understanding. You can develop your own custom annotation models using Watson Knowledge Studio (see side trip in Resources) that will help you identify industry and domain specific entities and relations in unstructured text.

Why use Natural Language Understanding?

In this quick video, learn the reasons to use the service.

Explore the service

Another quickie: Understand what Natural Language Understanding does and how it works.

Dive deeper into the technology

A longer video, in this one you’ll learn the functions of each feature of the service, one by one – categories, concepts, entities, relations, custom models, keywords, semantic roles, sentiment and emotions, and metadata.

Learn to use NLU

From the “Building with Watson” series, this video will take you on a journey to learn the development basics of using Natural Language Understanding. IBM Watson developer Joshua Elliott demonstrates how Watson’s Natural Language Understanding service works by analyzing text to extract meta-data from content and provides some common use cases.

Resources for you

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1 comment on"IBM Watson: Explore the Natural Language Understanding service"

  1. Dear IBM Waston NLU developers,

    I am reading your post on application of NLU (Natural Language Understanding). It seems exciting and can extract some interesting measures like sentiment or concepts in the content. This is great. It seems we can view the measurement result of your tool but we can not see some basic introduction to how this service is done. In other words, what is basic theoretical framework behind your tools. Other natural language processing applications or tools will let users roughly know how their tools work by presenting links to research papers. Will you please let me know the papers that deal with computations like sentiment, concepts and other functions in your service?

    Can you direct me to some papers that address NLU in your service?



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