Abstract
The lack of labeled data is one of the key problems in many current natural language processing tasks. Semi-supervised and unsupervised learning techniques have been explored as alternatives to fully-supervised learning; however, most techniques concentrate on classification problems as opposed to learning preferences or structured outputs that characterize a large class of natural language problems. This talk presents recent work on adapting semi-supervised and unsupervised learning schemes to ranking rather than classification tasks. Techniques that will be discussed include discovering better feature representations from unlabeled data, graph-based semi-supervised ranking, and constrained unsupervised ranking. Experimental results will be presented on applications in information retrieval, machine translation of spoken dialogues, and machine translation of multi-party meeting conversations.
Biography
Katrin Kirchhoff obtained an M.A. in English Linguistics and her PhD in Computer Science from the University of Bielefeld, Germany. Since 1999 she has been with the University of Washington, where she is currently a Research Associate Professor in the Electrical Engineering Department. Her research interests include speech recognition and natural language processing (in particular multilingual applications), machine translation, machine learning, and human-computer interfaces. She has authored over 60 conference and journal publications and is co-editor of a book on “Multilingual Speech Processing”. Katrin is a member of the Editorial Boards of the Speech Communication and Computer, Speech and Language journals and an Associate Editor for ACM Transactions in Speech and Language Processing. She is currently serving on the IEEE Speech Technical Committee.