Your SlideShare is downloading. ×
NewsReader: Automating detective work
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

NewsReader: Automating detective work

2,005

Published on

NewsReader presentation given at the PoliMedia symposium at VU University on 23 January 2013

NewsReader presentation given at the PoliMedia symposium at VU University on 23 January 2013

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
2,005
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
9
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Automating Detective Workdiscovering story lines on the Web dr. Willem Robert van Hage dr. Marieke van Erp prof. dr. Piek Vossen
  • 2. The NewsReader Project• Process financial news in 4 different European languages (English, Dutch, Spanish, Italian). Extract what happened to whom, when, and where. Align, storing provenance, not discarding any information. Distinguish unfolding story lines. Assist financial decision support by explaining current events.• Consortium • VU University Amsterdam • The University of the Basque Country • Fondazione Bruno Kessler • LexisNexis • ScraperWiki• Funded by EU FP7 programme, grant 316404• Jan. 2013 - Dec. 2015
  • 3. the problem• you will not notice the significance of an event if you don’t know the story behind it• the story is fragmented and parts are told in different ways• “How does the process of gathering understanding work?”
  • 4. examplethe Hua Feng enters the harbor and nobody cares Hua Feng This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  • 5. shipphotos.wordpress.com This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  • 6. www.tradewindsnews.com This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  • 7. en.wikipedia.org This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  • 8. www.trafigura.com “Our item headlined Success for the Guardian (26 April, page 2) erroneously linked the dumping of toxic waste in Ivory Coast from a vessel chartered by Trafigura with the deaths of a number of West Africans...” – Guardian This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  • 9. the task discover the events that preceded and possibly led to a current event and summarize these for a human user • investigate participating actors, places, moments in time and a limited number of relations between these • follow all leads in parallel • forage for information on the World Wide and Semantic Web • store the connections in an RDF graph, keeping precise track of the provenance of the connections • summarize and present the results as a story line
  • 10. “Volkswagen+takeover”: 1.8M Hits in Google
  • 11. Example Story Line (slide: Piek Vossen)
  • 12. (slide: Piek Vossen)
  • 13. The BIG problem (slide: Piek Vossen)
  • 14. a bit more detail• gather textual information from the news on the Web or gather knowledge on the Semantic Web that relate to the current event (Information Retrieval / Databases)• in the case of news items, extract (partial) event descriptions from the text (Natural Language Processing)• generalize event descriptions to a desirable level of abstraction (Logical/Spatiotemporal Reasoning)• construct a story line from a selection of the event descriptions (Summarization)• visualize the story line (Visualization) Will this provide a more efficient way to structure information and news?
  • 15. http://www.newsreader-project.eu/

×