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SUMMARY:Sequential pattern mining for robust event detection - Lecture
DTSTART:20181011T110000
DTEND:20181011T120000
DTSTAMP:20260406T224715Z
UID:4eaaeea5792ee03dc44e2d81b337a5b1460b1362fb9149cd5f44970f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Antoine Doucet - Université de la Rochelle\nDH Distingu
 ished Lecture Series\n\nTitle: Sequential pattern mining for robust event 
 detection\n\nAbstract:\nIn the age of open and big data\, the task of auto
 matically analysing numerous media in various format and multiple language
 s is getting all the more critical. The ability to quickly and efficiently
  analyse massive amounts of documents\, both digitised and digitally-born\
 , is crucial. With a history dating a few centuries and a current rate of 
 about hundreds of thousands of articles published every day\, newspapers r
 epresent a heterogeneous resource of great importance.\n\nThis talk will p
 resent an approach that is able to detect events from news using very limi
 ted external resources\, notably not requiring any form of linguistic anal
 ysis. By relying on the journalistic genre rather than on linguistic analy
 sis\, it is both able to process text written in any language\, and in a f
 ashion that is robust to noise (eg\, stemming from imperfect OCR). Applied
  for instance to epidemic event detection\, it is able to find what epidem
 ic diseases are active where\, in any language and in real time. Evaluated
  over 40 languages\, the DaNIEL system is on average able to find epidemic
  events faster than human experts. In this presentation\, we will further 
 explain how this work is being expanded to further domains and particularl
 y to the specific case of historical newspapers.\n\nContact: Impresso Proj
 ect - Dr. Maud Ehrmann
LOCATION:BC 410 https://plan.epfl.ch/?room==BC%20410
STATUS:CONFIRMED
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