Colloquium - Hovy

Natural Language Processing/Computational Linguistics is 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, and statistical processing followed that in the 1990s, we are beginning to see a new, and quite, interesting trend: 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?

Sponsored by the Institute of Cognitive Science.