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Watson Knowledge Studio Features
Facing a large number of domain-specific documents imported into WDS to start data analysis and now need to perform domain-specific information extraction? The goal of this recipe is to show you how to leverage data from Watson Discovery Service to be used as a basis for a Machine Learning annotator in Watson Knowledge Studio.
This code pattern uses food reviews to explain how to easily extract insights from raw review data. It walks you through a working example of a web application that queries and manipulates data from Watson Discovery. And, with the aid of custom models using Watson Knowledge Studio (WKS), the data has additional enrichments that provide improved insights for user analysis.
This code pattern describes how to analyze SMS messages using Watson Knowledge Studio and Watson Natural Language Understanding to extract entities in the data. Specifically, the code pattern explains how to use Watson Knowledge Studio to create and train a machine learning model using human annotated documents, integrating the machine model into an NLU service, and extracting domain-specific entities using this NLU service.
IBM Watson™ Natural Language Understanding together with Watson Knowledge Studio provides an effective way of identifying necessary information from unstructured documents. The result can be augmented with regular expressions, and personal data identified is provided a score based on which further processing or consuming can be done.
Analyze text to extract metadata from content such as concepts, entities, keywords, categories, sentiment, emotion, relations and semantic roles using natural language understanding.
Uncover connections in data by combining automated ingestion with advanced AI functions with IBM Watson Discovery.
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