Mathematics Colloquium



Department of Mathematics
University of Nebraska at Omaha


WHEN:
Friday, September 16, 2005 at 2:30 PM

WHERE:
Durham Science Center, Room 255(256)

WHAT:


Doug Downey, Oren Etzioni, and Stephen Soderland

Department of Computer Science and Engineering
University of Washington
Seattle, WA 98195-2350
{ddowney,etzioni,soderlan}@cs.washington.edu


will give a talk on

A Probabilistic Model of Redundancy in Information Extraction


ABSTRACT:
Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without using hand-tagged training examples. A fundamental problem for both UIE and supervised IE is assessing the probability that extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness? This paper introduces a combinatorial “balls-andurns” model that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating the model’s parameters in practice and demonstrate experimentally that for UIE the model’s log likelihoods are 15 times better, on average, than those obtained by Pointwise Mutual Information (PMI) and the noisy-or model used in previous work. For supervised IE, the model’s performance is comparable to that of Support Vector Machines, and Logistic Regression.


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