Seminar - Professor Richard Khoury

Wednesday, March 4, 2015 2:30 pm - 3:30 pm EST (GMT -05:00)

Speaker

Richard Khoury, Associate Professor, Department of Software Engineering, Lakehead University

Topic

Phonetic Understanding of Written Text

Abstract

This presentation will outline two algorithms to understand phonetically written text. Given a new word, the objective of these algorithms is to determine its pronunciation. The first algorithm does this by training a probabilistic model, and the second uses a set of decision rules. Two applications are presented, namely microtext normalization, where we match new words written online to similar-sounding English words, and alliteration analysis of medieval literature.

Speaker's biography

Richard Khoury received his Bachelor’s Degree and his Master’s Degree in Electrical and Computer Engineering from Laval University in 2002 and 2004 respectively, and his Doctorate in Electrical and Computer Engineering from the University of Waterloo in 2007. Since August 2008, he is a faculty member in the Department of Software Engineering at Lakehead University. Dr. Khoury’s primary area of research is natural language processing, but his research interests also include data mining, knowledge management, machine learning, and artificial intelligence. He was the guest editor for a special issue of the Journal of Emerging Technologies in Web Intelligence dedicated to the growing field of Web Data Mining. He has over 30 peer-reviewed publications and $400,000 in research funding, and has won a Contribution to Teaching Award at Lakehead University. He is an adjunct professor in the Department of Software Engineering at Laval University, where he is currently doing his sabbatical year. Off-campus, he has served as president of the Rotary Club of Thunder Bay (Fort William) in 2013-2014, and is a founding member of “Ohm Base”, Thunder Bay’s first hackerspace, a public community technology lab that contributes to the spreading of technology awareness in the community and to the fostering of local talent.