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SUMMARY:Reading and Reasoning with Vector Representations - Sebastian Ried
 el (UCL)
DTSTART:20170518T100000Z
DTEND:20170518T110000Z
UID:TALK71802@talks.cam.ac.uk
CONTACT:Mohammad Taher Pilehvar
DESCRIPTION:In recent years\, vector representations of knowledge have bec
 ome popular in NLP and beyond. They have at least two core benefits: reaso
 ning with (low-dimensional) vectors tends to lead to better generalisation
 \, and usually scales very well. But they raise their own set of questions
 : What type of inferences do they support? How can they capture asymmetry?
  How can explicit background knowledge be injected into vector-based archi
 tectures? How can we provide “proofs” that justify predictions? In thi
 s talk\, I sketch some initial answers to some of these questions based on
  our recent work. In particular\, I will illustrate how a vector space can
  simulate the workings of logic. 
LOCATION:Lecture Block\, room 2\, Sidgwick Site
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