Time: 2pm, Wed. March 3rd
Reception to follow
Where: Davis Auditorium, Schapiro Engineering Research Building
(For directions see http://www.cs.columbia.edu/resources/directions)
Statistical Models of Language
Department of Computer Science, Massachusetts Institute of Technology
A vast amount of text and speech data is now available in electronic
form, making methods that recover structure in this data increasingly
important. In this talk I'll describe work on machine learning
approaches for the analysis of natural language. The central question
addressed in the talk will be the following: how can we design
statistical models, and associated learning algorithms, that operate
over rich linguistic representations? As examples of work in this area,
I will describe models for syntax (parsing), semantics (mapping
sentences to underlying representations of their meaning) and machine
translation. While the focus of this talk will be on language, many of
the techniques I will describe are applicable to machine learning
problems in other fields, for example speech recognition, computer
vision, and computational biology.
Michael Collins is an associate professor of computer science at MIT.
His research is focused on topics including statistical parsing,
structured prediction problems in machine learning, and applications
including machine translation, dialog systems, and speech recognition.
His awards include a Sloan fellowship, an NSF career award, and best
paper awards at EMNLP (2002 and 2004), UAI (2004 and 2005), and CoNLL 2008.