Institute of Formal and Applied Linguistics
Charles University, Czech Republic
Faculty of Mathematics and Physics
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.