2. VRT: Use Case

3. Textual data: a lot of it VRT creates very large amounts news data (textual) –Mostly News or related –sporza.be –deredactie.be –cobra.be –…

4. Data management

5. Challenge: automated tagging & categorization Tagging and categorizing the content is a resource intensive task (manpower)! Tagging consistently with a team is a difficicult challenge! How to handle the existing archive?

6. Test case Content from: –sporza.be –deredactie.be –cobra.be Over 100.000 articles Automated Named Entitiy recognition Automated Categorization Available in search-interface

7. Named Entity Recognition

8. Categorization or Topic Selection

9. Why?

10. Get a better understanding of the metadata management of an organization like VRT

11. Showcase our solution

12. Explore opportunities with VRT & other partners

13. Benefits & Applications

14. Uniform annotations over all content Processing of backlog Time savings: –1 min article –100.000 articles –208 working days Personalized filtering Automated publishing Easy search Simplify content reuse

15. Zeticon MediaHaven MAM Media Asset Management Problematic content reuse Inefficient use of time Security & Rights Management Scalability MediaHaven Analytics Information retrieval Resource Intensive Consistency Scalability Language dependencies

16. www.mediahaven.com