报告人：Kenny Q. Zhu时间：2015 年 4 月 27 日（星期一）上午 9:30地点：张江校区软件楼 102 第二会议室联系人：肖仰华 (shawyh@fudan.edu.cn)
Abstract:
To judge how much a pair of words (or texts) are semantically related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely basedon co-occurrences within textual windows, and do not actively leverage cognitive human perceptions of relatedness.To bridge this perceptional gap, we propose to utilize free association as signals to capture such human perceptions. However, free association, being manually evaluated, has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparsity by constructing an association network of terms and concepts that combines signals from free association norms and five types of cooccurrences extracted from the rich structures of Wikipedia. Our evaluation results validate that simple algorithms on this network give competitive results in computing semantic relatedness between words and between short texts.
Bio:
Kenny Q. Zhu is an Distinguished Research Professor at Department of Computer Science and Engineering of Shanghai Jiao Tong University. He graduated with B.Eng (Hons) in Electrical Engineering in 1999 and PhD in Computer Science in 2005 from National University of Singapore. He was a postdoctoral researcher and lecturer from 2007 to 2009 at Princeton University. Prior to that, he was a software design engineer at Microsoft, Redmond, WA. From Feb 2010 to Aug 2010, he was a visiting professor at Microsoft Research Asia in Beijing. Kenny's main research interests are data and knowledge engineering, artificial intelligence and programming languages. He has published extensively in programming languages, AI and databases at top venues such as POPL, ICFP, SIGMOD, KDD, AAAI, CIKM and ICDE. He has served on the PC of WWW, CIKM, ECML, COLING, SAC, WAIM, APLAS and NDBC, etc. His research has been supported by NSF China, MOE China, Microsoft, Google, Oracle, Morgan Stanley and AstraZeneca. Kenny is the winner of the 2013 Google Faculty Research Award and 2014 DASFAA Best Paper Award.
