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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cardie, C.: Empirical Methods in Information Extraction. AI Magazine 18(4), 65–80 (1997)
Cowie, J., Lehnert, W.: Information Extraction. Communications of the Association of Computing Machinery 39(1), 80–91 (1996)
Freitag, D., McCallum, A.: Information Extraction with HMM Structures Learned by Stochastic Optimization. In: AAAI/IAAI, pp. 584–589 (2000)
Etzioni, O., et al.: Unsupervised named-entity extraction from the Web: An experimental study. Artificial Intelligence 165(1), 91–134 (2005)
Riloff, E., Jones, R.: Learning Dictionaries for Information Extraction by Multi-level Boot-strapping. In: AAAI 1999 (1999)
Brin, S.: Extracting Patterns and Relations from the World Wide Web. In: WebDB Workshop at 6th International Conference on Extending Database Technology, EDBT 1998, Valencia, Spain (1998)
Agichtein, E., Gravano, L.: Snowball: Extracting Relations from Large Plain-Text Collections. In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL) (2000)
Ravichandran, D., Hovy, E.: Learning Surface Text Patterns for a Question Answering System. In: 40th ACL Conference (2002)
Ciravegna, F.: Adaptive Information Extraction from Text by Rule Induction and Generalization. In: Proceedings of the 17th IJCAI 2001, Seattle, WA (2001)
Soderland, S.: Learning Information Extraction Rules for Semi-Structured and Free Text. Machine Learning 34(1-3), 233–272 (1999)
Hasegawa, T., Sekine, S., Grishman, R.: Discovering Relations among Named Entities from Large Corpora. In: ACL 2004 (2004)
Miller, G.: WordNet: An on-line lexical database. International Journal of Lexicography 3(4), 235–312 (1990)
Genkin, A., Lewis, D.D., Madigan, D.: Large-Scale Bayesian Logistic Regression for Text Categorization, pp. 1–41. DIMACS, New Brunswick (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feldman, R., Rosenfled, B., Soderland, S., Etzioni, O. (2006). Self-supervised Relation Extraction from the Web. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_84
Download citation
DOI: https://doi.org/10.1007/11875604_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)