EM Works for Pronoun-Anaphora Resolution
Speaker: Eugene Charniak
Institution: Brown University
Abstract:
EM (the Expectation Maximization Algorithm) is a well known technique for unsupervised learning (where one does not have any hand labeled solutions available, but instead one must learn from the raw text). Unfortunately EM is known to fail to find good solutions in many (most?) applications on which it is tried. In this talk we present some recent work on using EM to learn how to resolve pronoun-anaphora, e.g., determining that "the dog" is the antecedent of "he" and "his" in "When Sally fed the dog he wagged his tail". For this application EM works strikingly well, determining tens of thousands of parameters and resulting in a program that produces state of the art performance.
Eugene Charniak is University Professor of Computer Science at Brown
University and past chair of the department. He received his
A.B. degree in Physics from University of Chicago, and a Ph.D. from
M.I.T. in Computer Science. He has published four books the most
recent being Statistical Language Learning. He is a Fellow of the
American Association of Artificial Intelligence and was previously a
Councilor of the organization. His research has always been in the
area of language understanding or technologies which relate to it.
Over the last 15 years years he has been interested in statistical
techniques for many areas of language processing including parsing,
discourse and anaphora.
