2008年清华大学信息技术研究院系列学术报告16

Biography

Eduard Hovy directs the Natural Language Research Group at USC’s Information Sciences Institute and serves as Deputy Director of the Intelligent Systems Division and as research associate professor of the Computer Science Department. He also directs the DHS Center for Knowledge Integration and Discovery at the University of Southern California and is Director of Research of its Digital Government Research Center. He completed a Ph.D. in Computer Science (Artificial Intelligence) at Yale University in 1987. His research focuses on information extraction, automated text summarization, the semi-automated construction of large lexicons and ontologies, machine translation, question answering, and digital government. Dr. Hovy regularly serves in an advisory capacity to funders of NLP research in the US and EU. He is the author or co-editor of five books and over 180 technical articles. In 2001 Dr. Hovy served as President of the Association for Computational Linguistics (ACL) and in 2001–03 as President of the International Association of Machine Translation (IAMT); he currently serves as President of the Digital Government Society of North America (DGSNA). Dr. Hovy regularly co-teaches a specialized course in the Computer Science Department of the University of Southern California, as well as occasional short courses on MT and other topics at universities and conferences. He actively advises Ph.D. students, student visitors, and faculty on sabbatical, and has served on the Ph.D. and M.S. committees for students from USC, Carnegie Mellon University, Taiwan National U, the Universities of Toronto, Karlsruhe, Pennsylvania, Stockholm, Waterloo, Nijmegen, Pretoria, and Ho Chi Minh City.

Abstract

Natural Language text and speech processing (Computational Linguistics) is just over 50 years old, and is still continuously evolving—not only in its technical subject matter, but in the basic questions being asked and the style and methodology being adopted to answer them. As unification followed finite-state technology in the 1980s, statistical processing followed that in the 1990s, and large-scale processing is increasingly being adopted (especially for commercial NLP) in this decade, a new and quite interesting trend is emerging: a split of the field into three somewhat complementary and rather different directions, each with its own goals, evaluation paradigms, and methodology. The resource creators focus on language and the representations required for language processing; the learning researchers focus on algorithms to effect the transformation of representation required in NLP; and the large-scale hackers produce engines that win the NLP competitions. But where the latter two trends have a fairly well-established methodology for research and papers, the first doesn’t, and consequently suffers in recognition and funding. In the talk, I describe each trend, provide some examples of the first, and conclude with a few general questions, including: Where is the heart of NLP? What is the nature of the theories developed in each stream (if any)? What kind of work should one choose to do if one is a grad student today?