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NewsReader: Automating detective work


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

Published in: Technology
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NewsReader: Automating detective work

  1. 1. Automating Detective Workdiscovering story lines on the Web dr. Willem Robert van Hage dr. Marieke van Erp prof. dr. Piek Vossen
  2. 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. 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. 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. 5. This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  6. 6. This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  7. 7. This research was funded by COMMIT/Metis (NL) and COMBINE (USA ONRG).
  8. 8. “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. 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. 10. “Volkswagen+takeover”: 1.8M Hits in Google
  11. 11. Example Story Line (slide: Piek Vossen)
  12. 12. (slide: Piek Vossen)
  13. 13. The BIG problem (slide: Piek Vossen)
  14. 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. 15.