A probabilistic model of redundancy in information extraction

Doug Downey, Oren Etzioni, Stephen Soderland

Research output: Contribution to journalConference articlepeer-review

114 Scopus citations


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-and-urns" 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.

Original languageEnglish (US)
Pages (from-to)1034-1041
Number of pages8
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: Jul 30 2005Aug 5 2005

ASJC Scopus subject areas

  • Artificial Intelligence


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