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UID:20260602T184126EDT-2710F4sWzo@132.216.98.100
DTSTAMP:20260602T224126Z
DESCRIPTION:Title:(Almost) all of Entity Resolution.\n\nAbstract: Whether t
 he goal is to estimate the number of people that live in a congressional d
 istrict\, to estimate the number of individuals that have died in an armed
  conflict\, or to disambiguate individual authors using bibliographic data
 \, all these applications have a common theme — integrating information fr
 om multiple sources. Before such questions can be answered\, databases mus
 t be cleaned and integrated in a systematic and accurate way\, commonly kn
 own as record linkage\, de-duplication\, or entity resolution. In this art
 icle\, we review motivational applications and seminal papers that have le
 d to the growth of this area. Specifically\, we review the foundational wo
 rk that began in the 1940’s and 50’s that have led to modern probabilistic
  record linkage. We review clustering approaches to entity resolution\, se
 mi- and fully supervised methods\, and canonicalization\, which are being 
 used throughout industry and academia in applications such as human rights
 \, official statistics\, medicine\, citation networks\, among others. Fina
 lly\, we discuss current research topics of practical importance. This is 
 joint work with Olivier Binette.\n\nweb : http://crm.umontreal.ca/colloque
 -sciences-mathematiques-quebec/index.html#csmq\n
DTSTART:20221125T193000Z
DTEND:20221125T213000Z
SUMMARY:Rebecca Steorts\, Duke University
URL:/mathstat/channels/event/rebecca-steorts-duke-univ
 ersity-343763
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