Machine Learning Models and Linguistic Representations
Chris Manning's web page: http://nlp.stanford.edu/~manning/
The application of statistical and machine-learning techniques revolutionized NLP and placed the field on a more scientific and broader base. And there continues to be substantial progress, in the progress from mainly generative to often discriminative models, and new ideas such as extensive joint modeling and maximum margin techniques. But it's never been the whole story: much of the difference between systems has always been in the representations and the choice of features of different systems. Developing these is a matter of doing good linguistics (if in a somewhat more empirical and quantitative way than theoretical linguists in the U.S. normally consider). Looking to the future, it seems to me less likely that profound progress is going to come from new machine learning technology, and much more likely that progress is going to come from better linguistic representations. In particular, issues of representation become more prominent when people are focusing on deeper problems of NLP involving semantics and discourse. In this talk I will illustrate these themes by looking at recent work of mine and others on information extraction, parsing, grammar induction, and semantic representations.
Chris Manning is an Assistant Professor of Computer Science and Linguistics at Stanford University. He received his Ph.D. from Stanford University in 1995, and held faculty positions in the Computational Linguistics Program at Carnegie Mellon University (1994-1996) and in the University of Sydney Linguistics Department (1996-1999) before returning to Stanford. He is a Terman Fellow and recipient of an IBM Faculty Award. His recent work has concentrated on statistical parsing, grammar induction, and probabilistic approaches to problems such as word sense disambiguation, part-of-speech tagging, and named entity recognition, with an emphasis on complementing leading machine learning methods with use of rich linguistic features. Manning coauthored the leading textbook on statistical approaches to NLP (Manning and Schuetze 1999) and (with Dan Klein) received the best paper award at the Association for Computational Linguistics 2003 meeting for the paper Accurate Unlexicalized Parsing.