IBM has announced enhancements to the natural language processing (NLP) capabilities of Watson Discovery.
Watson Discovery is an AI-powered intelligent search and text-analytics platform that can retrieve critical information buried in enterprise data.
In one case study, Woodside Energy had no way to retrieve the 30 years’ worth of value engineering and drilling knowledge that was buried in unstructured documentation. Using the existing NLP capabilities of Watson Discovery, the firm reportedly cut research time by more than 75 percent.
Among the new enhancements planned for Watson Discovery are:
- Pre-trained document structure understanding: Watson Discovery’s Smart Document Understanding feature now includes a new pre-trained model designed to automatically understand the visual structure and layout of a document without additional training from a developer or data scientist.
- Automatic text pattern detection: A new advanced pattern creation feature is available in beta that helps users to quickly identify business-specific text patterns within their documents. It can start learning the underlying text patterns from as little as two examples and then refines the pattern based on user feedback.
- Advanced NLP customisation capabilities: With a new custom entity extractor feature, IBM is simplifying the process of training NLP models to identify highly-customised, business-specific words by reducing the data prep effort, simplifying labeling with active learning and bulk annotation capabilities, and enabling simple model deployment to accelerate training time.
“The stream of innovation coming to IBM Watson from IBM Research is why global businesses in the fields of financial services, insurance, and legal services turn to IBM to help detect emerging business trends, gain operational efficiency and empower their workers to uncover new insights,” said Daniel Hernandez, General Manager of Data and AI, IBM.
“The pipeline of natural language processing innovations we’re adding to Watson Discovery can continue to provide businesses with the capabilities to more easily extract the signal from the noise and better serve their customers and employees.”