Self-supervised relation extraction from the web

Authors:
Ronen Feldman;Benjamin Rosenfled;Stephen Soderland;Oren Etzioni
Affiliations:
Computer Science Department, Bar-Ilan University, Ramat Gan, Israel;Computer Science Department, Bar-Ilan University, Ramat Gan, Israel;Department of Computer Science, Washington University, Seattle, WA;Department of Computer Science, Washington University, Seattle, WA
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 10
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Abstract

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.