[Colloquium] JOB TALK TODAY: Hoifung Poon, University of Washington

[Katie Casey]

DEPARTMENT OF COMPUTER SCIENCE

UNIVERSITY OF CHICAGO

Date: Wednesday, January 12, 2011
Time: 2:30 p.m.
Place: Ryerson 251, 1100 E. 58th Street

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Speaker:		Hoifung Poon

From:		University of Washington

Web page:	http://www.cs.washington.edu/homes/hoifung/

Title: 		Markov Logic in Machine Reading

Abstract:  	A long-standing goal of AI and natural language processing is to
harness human knowledge by automatically understanding texts. Known as
machine reading, it has become increasingly urgent with the rise of
billions of web documents. To represent the acquired knowledge that is
complex and heterogeneous, we need first-order logic. To handle the
inherent uncertainty and ambiguity in extracting and reasoning with
knowledge, we need probability. Combining the two has led to rapid
progress in the emerging field of statistical relational learning. In
this talk, I will show that statistical relational learning offers
promising solutions for machine reading. I will present Markov logic,
which is a leading unifying framework for statistical relational
learning, and has spawned a number of successful applications for
machine reading. In particular, I will present USP, an end-to-end
machine reading system that can read text, extract knowledge and
answer questions, all without any labeled examples. To resolve
linguistic variations for the same meaning, USP recursively clusters
expressions that are composed with or by similar expressions. In a
machine reading experiment, USP extracted five times as many correct
answers compared to state-of-the-art systems such as TextRunner, and
raised accuracy from below 60% to 91%.

Host: 		John Goldsmith

Refreshments will be served following the talk at 3:30 in Ryerson 255.