Layman's Talk: Entities of Interest --- Discovery in Digital Traces
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Layman's Talk: Entities of Interest --- Discovery in Digital Traces
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Slides of the 10 min layman's talk that preceded my PhD defence. In this talk I summarize ~4yrs of research in 10 minutes, so it's a very high-level overview.
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Layman's Talk: Entities of Interest --- Discovery in Digital Traces
- 1. Program Layman’s talk Committee comes and grills me Committee retreats Ceremony Reception downstairs 10:00 10:15 11:00 ~11:15 ~11:30— 12:30
- 5. Entities of Interest Discovery in Digital Traces
- 6. Entities of Interest Discovery in Digital Traces Object of study
- 7. Entities of Interest Discovery in Digital Traces Object of study Task
- 8. Entities of Interest Discovery in Digital Traces Object of study Task Domain
- 9. Entities of Interest Discovery in Digital Traces
- 10. Entities of Interest Discovery in Digital Traces
- 11. Entities of Interest Discovery in Digital Traces
- 12. Entities of Interest Discovery in Digital Traces
- 13. Entities of Interest Discovery in Digital Traces
- 14. Entities of Interest Discovery in Digital Traces
- 15. Entities of Interest Discovery in Digital Traces
- 16. • Gain new insights/discover new information Entities of Interest Discovery in Digital Traces
- 17. • Gain new insights/discover new information • Answer questions: Who was involved? What happened? Where, when and why did it happen? Entities of Interest Discovery in Digital Traces
- 18. • Gain new insights/discover new information • Answer questions: Who was involved? What happened? Where, when and why did it happen? Entities of Interest Discovery in Digital Traces
- 19. Entities of Interest Discovery in Digital Traces
- 20. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence”
- 21. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
- 22. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
- 23. Entities of Interest Discovery in Digital Traces • “Things with distinct and independent existence” • Real-world entities central to answering 5 W’s.
- 24. Challenges
- 25. Challenges • Language is “noisy”
- 26. Challenges • Language is “noisy” • “Big Data”
- 27. Methods
- 29. Methods • Information Retrieval • Searching & finding things
- 30. Methods • Information Retrieval • Searching & finding things • Natural Language Processing
- 31. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language
- 32. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language • Machine Learning
- 33. Methods • Information Retrieval • Searching & finding things • Natural Language Processing • (automated) ’understanding’ of language • Machine Learning • Using programs that ‘learn’ to do something
- 35. Two types of Entities of Interest
- 36. Two types of Entities of Interest Part 1: Entities in digital traces
- 37. Two types of Entities of Interest Part 1: Entities in digital traces • Content/data
- 38. Two types of Entities of Interest Part 1: Entities in digital traces • Content/data Part 2: Entities that produce digital traces
- 39. Two types of Entities of Interest Part 1: Entities in digital traces • Content/data Part 2: Entities that produce digital traces • Context/metadata
- 40. Part I Part 1: Entities in digital traces
- 41. Part I Part 1: Emerging Entities in digital traces
- 44. First mention
- 45. Wikipedia Page CreatedFirst mention
- 46. Wikipedia Page CreatedFirst mention Are there common temporal patterns in how entities emerge in online text streams?
- 47. Wikipedia Page CreatedFirst mention Are there common temporal patterns in how entities emerge in online text streams?Yes!
- 50. Can we leverage prior knowledge of entities to bootstrap the discovery of new entities?
- 51. Can we leverage prior knowledge of entities to bootstrap the discovery of new entities? Yes!
- 54. *****
- 55. ***** Can we leverage collective intelligence to construct entity representations for in- creased retrieval effectiveness of entities of interest?
- 56. ***** Can we leverage collective intelligence to construct entity representations for in- creased retrieval effectiveness of entities of interest? Yes!
- 57. Part II Entities of Interest: Producers of digital traces
- 58. Part II Entities of Interest: Producers of digital traces Aim: Study and predict real-world activity from digital traces
- 59. Part II Entities of Interest: Producers of digital traces Aim: Study and predict real-world activity from digital traces Two case-studies
- 63. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl Can we predict email communication through modeling email content and communication graph properties?
- 64. d.p.graus@uva.nl z.ren@uva.nl derijke@uva.nl Can we predict email communication through modeling email content and communication graph properties? Yes!
- 68. Creation times Notification times Creation times Notification times
- 69. Creation times Notification times Creation times Notification times Can we identify patterns in the times at which people create reminders, and, via notification times, when the associated tasks are to be executed?
- 70. Creation times Notification times Creation times Notification times Can we identify patterns in the times at which people create reminders, and, via notification times, when the associated tasks are to be executed? Yes!
- 71. In Summary • Part 1: We propose methods for analyzing, predicting, and retrieving emerging entities • Part 2: We propose methods for predicting future activity by leveraging digital traces.
- 73. Program Committee comes and grills me Committee retreats Ceremony Reception downstairs 10:15 11:00 ~11:15 ~11:30— 12:30