Tuesday, September 25, 2012 - 15:00
Room: 

Learning under Bias in NLP

Abstract: In NLP we rely on manually annotated data, e.g. treebanks. Such data is hard to come by, explaining recent interests in semi-supervised NLP. However, our labeled data is also (almost always) extremely biased. This talk presents bias correction techniques and discusses their applicability in NLP.

CV: 
Anders Søgaard did his Ph.D. in 2007 at the University of Copenhagen in mathematical linguistics. He has been a Senior Researcher at the University of Potsdam and now works as an Associate Professor at the University of Copenhagen. He was recently awarded an European Research Council Starting Grant.