Entity extraction is a core capability of text analytics, so I thought I’d step through an example of how it works.
Note: Learn about the all the different text analytics capabilities in my last post Text Analytics: A Spectrum of Enrichment Techniques.
Consider the following text that talks about Apple and Berkshire Hathaway. Entity extraction lets you pick out the entities in the text (companies, persons, locations) and then help you understand how they are related to other.
For cognitive search to work, you need text analytics, which is why it’s a key component of the Attivio Cognitive Search Platform. There is a range of capabilities within text analytics to understand, so I thought I’d take you through them and explain how they work.
These days, there’s pretty much a trade show for everything — and predictive analytics is no exception. The most recent stateside edition of Predictive Analytics World™ took place in Chicago in June of this year. And predictive maintenance occupied a prominent place on the agenda. One presentation concerned failure and fault detection for Rolls Royce.
A recent study by IDC, Data Age 2025: The Evolution of Data to Life-Critical, projects that the amount of data subject to analysis will grow by a factor of 50 between now and 2025. Further, the amount of analyzed data affected by cognitive systems will grow by a factor of 100 to 1.4ZB (zettabytes).