Think!!

IBM Watson™ Knowledge Studio to Create a machine-learning model that Understands the linguistic nuances, Meaning, and * Relationships Specific to your industry

Or

Create a rule-based model that finds entities in documents based on rules that you define.

To become a subject matter expert in a given industry or domain, Watson must be trained.

The task of training Watson can be facilitated by Watson Knowledge Studio.

Build a machine-learning model :

Watson Knowledge Studio provides easy-to-use tools for annotating unstructured domain literature, and uses those annotations to create a custom machine-learning model that understands the language of the domain.

The accuracy of the model improves through * iterative testing.

Ultimately resulting in an algorithm that can learn from the patterns that it sees and recognize those patterns in large collections of new documents.

The finished machine-learning model can be deployed to other solutions.

1. Based on a set of domain-specific source documents, the team creates a type system that defines entity types and relation types for the information of interest to the application that will use the model.

2. A group of 2 or more human annotators a small set of source documents to

3. label words that represent entity types,

4. to identify relation types where the text identifies relationships between entity mentions.

5. to define coreferences, which identify different mentions that refer to the same thing, that is, the same entity.

6. The ground truth is used to train a model.

7. The trained model is used to find entities, relations, and coreferences in new, never-seen-before documents.

Creating a machine-learning annotator :

Create a machine-learning annotator that trains a model you can use to identify entities, coreferences, and relationships of interest in new documents.

Machine-learning model creation workflow:

Word Net

• New relations need to be identified. *