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DTSTART:20050403T070000
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DTSTART:20051030T060000
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UID:69b21e6f2839a
DTSTART;TZID=America/Toronto:20060120T120000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20060120T120000
URL:https://uwaterloo.ca/artificial-intelligence-group/events/ai-seminar-na
 ive-bayes-modelling-proper-smoothing
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2306C (AI lab) Waterloo ON N2L 3G1 Canada
SUMMARY:AI seminar: Naive Bayes modelling with proper smoothing for\ninform
 ation extraction
CLASS:PUBLIC
DESCRIPTION:Speaker: Zhenmei Gu\n\nInformation Extraction (IE) summarizes a
  collection of textual\ndocuments into a structual representation by ident
 ifying specific\nfacts from text. The naive Bayes model is one of the firs
 t statistical\nmodels that have been applied to IE for learning extraction
  patterns\nfrom labelled data. In spite of the simplicity and the populari
 ty of\nthe naive Bayes model\, we have observed a formulation problem in\n
 previous work on naive Bayes IE. In this talk\, we present a formal\nnaive
  Bayes modelling for IE\, by which the induced formula for the\nfiller pro
 bability estimation is more theoretically sound.
DTSTAMP:20260312T020119Z
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